Journal articles: 'Decorrelation noise'

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Palmer, Alan R., Dan Jiang, and David McAlpine. "Desynchronizing Responses to Correlated Noise: A Mechanism for Binaural Masking Level Differences at the Inferior Colliculus." Journal of Neurophysiology 81, no. 2 (February 1, 1999): 722–34. http://dx.doi.org/10.1152/jn.1999.81.2.722.

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Desynchronizing responses to correlated noise: a mechanism for binaural masking level differences at the inferior colliculus. We examined the adequacy of decorrelation of the responses to dichotic noise as an explanation for the binaural masking level difference (BMLD). The responses of 48 low-frequency neurons in the inferior colliculus of anesthetized guinea pigs were recorded to binaurally presented noise with various degrees of interaural correlation and to interaurally correlated noise in the presence of 500-Hz tones in either zero or π interaural phase. In response to fully correlated noise, neurons’ responses were modulated with interaural delay, showing quasiperiodic noise delay functions (NDFs) with a central peak and side peaks, separated by intervals roughly equivalent to the period of the neuron’s best frequency. For noise with zero interaural correlation (independent noises presented to each ear), neurons were insensitive to the interaural delay. Their NDFs were unmodulated, with the majority showing a level of activity approximately equal to the mean of the peaks and troughs of the NDF obtained with fully correlated noise. Partial decorrelation of the noise resulted in NDFs that were, in general, intermediate between the fully correlated and fully decorrelated noise. Presenting 500-Hz tones simultaneously with fully correlated noise also had the effect of demodulating the NDFs. In the case of tones with zero interaural phase, this demodulation appeared to be a saturation process, raising the discharge at all noise delays to that at the largest peak in the NDF. In the majority of neurons, presenting the tones in π phase had a similar effect on the NDFs to decorrelating the noise; the response was demodulated toward the mean of the peaks and troughs of the NDF. Thus the effect of added tones on the responses of delay-sensitive inferior colliculus neurons to noise could be accounted for by a desynchronizing effect. This result is entirely consistent with cross-correlation models of the BMLD. However, in some neurons, the effects of an added tone on the NDF appeared more extreme than the effect of decorrelating the noise, suggesting the possibility of additional inhibitory influences.

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Tuzlukov, V. P. "Two approaches to multiuser detection over fading channels." Doklady BGUIR 19, no. 1 (February 23, 2021): 11–20. http://dx.doi.org/10.35596/1729-7648-2021-19-1-11-20.

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In this paper, two different receiver structures to multiuser detection that are appropriate for the code-division multiple-access systems with antenna arrays in fading channels are investigated and compared. We analyze and compare the performance of the two different multiuser detection structures for uplink or downlink channels. The number of elements of receiving antenna array may be limited in the downlink channel due to the small size of receivers. We assume a synchronous system, but it can be easily extended to an asynchronous system. The first approach is based on the distributed decorrelator where the signal decorrelation is performed by each receiving antenna element independently and decorrelated outputs are combined according to the maximum ratio. The second approach is the central decorrelator where the signal decorrelation is performed once collectively on the outputs from all elements of receiving antenna array. Both decorrelators provide the same performance in the additive white Gaussian noise channels. The distributed decorrelator provides the better performance in flat fading channels. We employ the decorrelator to demonstrate our results. The results discussed in the present paper can be extended to other configurations such as the blind adaptive space-time multiuser detection.

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Jiang, Guoqing, Chao Sun, and Lei Xie. "Diagonal Denoising for Spatially Correlated Noise Based on Diagonalization Decorrelation in Underwater Radiated Noise Measurement." Journal of Marine Science and Engineering 10, no. 4 (April 5, 2022): 502. http://dx.doi.org/10.3390/jmse10040502.

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In underwater radiated noise measurement using a vertical linear array, a diagonalization-decorrelation-based diagonal denoising method is proposed to improve the denoising effect for spatially correlated noise. Firstly, the ambient noise cross-spectral matrix is measured without the radiated noise source. Subsequently, the eigenvector matrix of the ambient noise cross-spectral matrix is utilized to implement a unitary transformation for the received data, which eliminates the correlation of the received noise and transforms the received noise cross-spectral matrix into a diagonal matrix, then the noise components are removed by diagonal denoising. Finally, the denoised cross-spectral matrix is used to estimate the power of the radiated noise by beamforming. Consequently, the influence of spatially correlated noise on radiated noise measurement is reduced. The effectiveness of the proposed method is validated and compared with the diagonal denoising method and the whitening-decorrelation-based diagonal denoising method via numerical simulations and experimental data. Under the ideal condition, the noise reduction performances of the proposed method and the whitening-decorrelation-based diagonal denoising method are equal and better than that of the diagonal denoising method. In practice, the number of snapshots is limited, so there is an inevitable mismatch between the ambient noise cross-spectral matrix and the received noise cross-spectral matrix due to the randomness of noise. The mismatch results in imperfect whitening and diagonalization, which reduces the denoising effect. However, the simulation results indicate that the proposed method still reduces more correlated noise and has a better performance on underwater radiated noise measurement compared with the diagonal denoising method and the whitening-decorrelation-based diagonal denoising method even if the number of snapshots is finite.

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Arienzo, Alberto, Fabrizio Argenti, Luciano Alparone, and Monica Gherardelli. "Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data." Remote Sensing 12, no. 2 (January 20, 2020): 331. http://dx.doi.org/10.3390/rs12020331.

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In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR processor to avoid defocusing of targets caused by Gibbs effects. Since each polarimetric channel is focused independently of the others, the noise-whitening procedure can be performed applying the decorrelation stage to each channel separately. Equivalently, the noise-whitening stage is applied to each element of the scattering matrix before any multilooking operation, either coherent or not, is performed. In order to evaluate the impact of a spatial decorrelation of the noise on the performance of polarimetric despeckling filters, we make use of simulated PolSAR data, having user-defined polarimetric features. We optionally introduce a spatial correlation of the noise in the simulated complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure and compare the accuracy of both despeckling and polarimetric features estimation for the three following cases: uncorrelated, correlated, and decorrelated images. Simulation results showed a steady improvement of performance scores, most notably the equivalent number of looks (ENL), which increased after decorrelation and closely attained the value of the uncorrelated case. Besides ENL, the benefits of the noise decorrelation hold also for polarimetric features, whose estimation accuracy is diminished by the correlation. Also, the trends of simulations were confirmed by qualitative results of experiments carried out on a true Radarsat-2 image.

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Chen, Yaogang, Qian Sun, and Jun Hu. "Quantitatively Estimating of InSAR Decorrelation Based on Landsat-Derived NDVI." Remote Sensing 13, no. 13 (June 22, 2021): 2440. http://dx.doi.org/10.3390/rs13132440.

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As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage.

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Sathesh, Sathesh, and Dr J. Samuel Manoharan. "De-correlation stretch filtering approach for effective Poisson reduction in galaxy Images." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 9 (December 5, 2013): 2987–93. http://dx.doi.org/10.24297/ijct.v11i9.3408.

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Noise reduction is one of the most important processes to enhance the quality of images. This paper proposes a statistical filter, the decorrelation stretch filter for the reduction of Poisson noise that occurs frequently in galaxy images. The primary purpose of decorrelation stretch is visual enhancement. Decorrstretch is applied to the three band images but can also work on arbitrary number of bands. This filter enhances the color separation of an image with significant band-band correlation. Effectiveness of the proposed filter is compared on the basis of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).

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Jung, H. Y., D. Y. Kim, and C. K. Un. "Frame decorrelation for noise-robust speech recognition." Electronics Letters 32, no. 13 (1996): 1163. http://dx.doi.org/10.1049/el:19960808.

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Pradhan, Somanath, Xiaojun Qiu, and Jinchen Ji. "A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal." Applied Sciences 9, no. 15 (July 25, 2019): 2973. http://dx.doi.org/10.3390/app9152973.

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The presence of control signal feedback to the reference microphone in feedforward active control systems deteriorates the control performance. A four-stage method is proposed in this paper to carry out online feedback path modelling with the control signal. It consists of controller initialization, feedback path modelling using decorrelation filters, active control operation, and feedback path change detection for maintaining the control operation. In contrast to the existing auxiliary noise injection method, the proposed method uses five switches and three thresholds to control and maintain the system stability by avoiding the interference between control operation and feedback path modelling, and adaptive decorrelation filters are used to increase the feedback path modelling performance. Simulation results reveal that the proposed method is capable of tracking feedback path changes without injecting any auxiliary noise and maintaining the noise reduction performance and stability of the system.

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Wang, Xinmei, Leimin Wang, Longsheng Wei, and Feng Liu. "Estimation of Object Motion State Based on Adaptive Decorrelation Kalman Filtering." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 4 (July 20, 2019): 749–57. http://dx.doi.org/10.20965/jaciii.2019.p0749.

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To estimate the motion state of object feature point in image space, an adaptive decorrelation Kalman filtering model is proposed in this paper. The model is based on the Kalman filtering method. A first-order Markov sequence model is used to describe the colored measurement noise. To eliminate the colored noise, the measurement equation is reconstructed and then a cross-correlation between the process noise and the newly measurement noise is established. To eliminate the noise cross-correlation, a reconstructed process equation is proposed. According to the new process and measurement equations, and the noise mathematical characteristics of the standard Kalman filtering method, the parameters involved in the new process equation can be acquired. Then the noise cross-correlation can be successfully eliminated, and a decorrelation Kalman filtering model can be obtained. At the same time, for obtaining a more accurate measurement noise variance, an adaptive recursive algorithm is proposed to update the measurement noise variance based on the correlation method. It overcomes the limitations of traditional correlation methods used for noise variance estimation, thus, a relatively accurate Kalman filtering model can be obtained. The simulation shows that the proposed method improves the estimation accuracy of the motion state of object feature point.

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Aghanim, N., M. Ashdown, J. Aumont, C. Baccigalupi, M. Ballardini, A. J. Banday, R. B. Barreiro, et al. "Planck intermediate results." Astronomy & Astrophysics 599 (February 28, 2017): A51. http://dx.doi.org/10.1051/0004-6361/201629164.

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The characterization of the Galactic foregrounds has been shown to be the main obstacle in thechallenging quest to detect primordial B-modes in the polarized microwave sky. We make use of the Planck-HFI 2015 data release at high frequencies to place new constraints on the properties of the polarized thermal dust emission at high Galactic latitudes. Here, we specifically study the spatial variability of the dust polarized spectral energy distribution (SED), and its potential impact on the determination of the tensor-to-scalar ratio, r. We use the correlation ratio of the CBBℓ angular power spectra between the 217 and 353 GHz channels as a tracer of these potential variations, computed on different high Galactic latitude regions, ranging from 80% to 20% of the sky. The new insight from Planck data is a departure of the correlation ratio from unity that cannot be attributed to a spurious decorrelation due to the cosmic microwave background, instrumental noise, or instrumental systematics. The effect is marginally detected on each region, but the statistical combination of all the regions gives more than 99% confidence for this variation in polarized dust properties. In addition, we show that the decorrelation increases when there is a decrease in the mean column density of the region of the sky being considered, and we propose a simple power-law empirical model for this dependence, which matches what is seen in the Planck data. We explore the effect that this measured decorrelation has on simulations of the BICEP2-Keck Array/Planck analysis and show that the 2015 constraints from these data still allow a decorrelation between the dust at 150 and 353 GHz that is compatible with our measured value. Finally, using simplified models, we show that either spatial variation of the dust SED or of the dust polarization angle are able to produce decorrelations between 217 and 353 GHz data similar to the values we observe in the data.

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Pelgrims, V., S. E. Clark, B. S. Hensley, G. V. Panopoulou, V. Pavlidou, K. Tassis, H. K. Eriksen, and I. K. Wehus. "Evidence for line-of-sight frequency decorrelation of polarized dust emission in Planck data." Astronomy & Astrophysics 647 (March 2021): A16. http://dx.doi.org/10.1051/0004-6361/202040218.

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If a single line of sight (LOS) intercepts multiple dust clouds with different spectral energy distributions and magnetic field orientations, then the frequency scaling of each of the Stokes Q and U parameters of the thermal dust emission may be different, a phenomenon we refer to as LOS frequency decorrelation. We present first evidence for LOS frequency decorrelation in Planck data using independent measurements of neutral-hydrogen (HI) emission to probe the 3D structure of the magnetized interstellar medium (ISM). We use HI-based measurements of the number of clouds per LOS and the magnetic field orientation in each cloud to select two sets of sightlines: (i) a target sample of pixels that are likely to exhibit LOS frequency decorrelation and (ii) a control sample of pixels that lack complex LOS structure. We test the null hypothesis that LOS frequency decorrelation is not detectable in Planck 353 and 217 GHz polarization data at high Galactic latitudes. We reject the null hypothesis at high significance based on data that show that the combined effect of polarization angle variation with frequency and depolarization are detected in the target sample. This detection is robust against the choice of cosmic microwave background (CMB) map and map-making pipeline. The observed change in polarization angle due to LOS frequency decorrelation is detectable above the Planck noise level. The probability that the detected effect is due to noise alone ranges from 5 × 10−2 to 4 × 10−7, depending on the CMB subtraction algorithm and treatment of residual systematic errors; correcting for residual systematic errors consistently increases the significance of the effect. Within the target sample, the LOS decorrelation effect is stronger for sightlines with more misaligned magnetic fields, as expected. With our sample, we estimate that an intrinsic variation of ~15% in the ratio of 353 to 217 GHz polarized emission between clouds is sufficient to reproduce the measured effect. Our finding underlines the importance of ongoing studies to map the three-dimensional structure of the magnetized and dusty ISM that could ultimately help component separation methods to account for frequency decorrelation effects in CMB polarization studies.

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Mancini-Terracciano, C., and M. Vignati. "Noise correlation and decorrelation in arrays of bolometric detectors." Journal of Instrumentation 7, no. 06 (June 26, 2012): P06013. http://dx.doi.org/10.1088/1748-0221/7/06/p06013.

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Zhao, Sipei, Eva Cheng, Xiaojun Qiu, Ian Burnett, and Jacob Chia-chun Liu. "Spatial decorrelation of wind noise with porous microphone windscreens." Journal of the Acoustical Society of America 143, no. 1 (January 2018): 330–39. http://dx.doi.org/10.1121/1.5021335.

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Picart, Pascal. "Recent advances in speckle decorrelation modeling and processing in digital holographic interferometry." Photonics Letters of Poland 13, no. 4 (December 30, 2021): 73. http://dx.doi.org/10.4302/plp.v13i4.1126.

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Digital holography, and especially digital holographic interferometry, is a powerful approach for the characterization of modifications at the surface or in the volume of objects. Nevertheless, the reconstructed phase data from holographic interferometry is corrupted by the speckle noise. In this paper, we discuss on recent advances in speckle decorrelation noise removal. Two main topics are considered. The first one presents recent results in modelling the decorrelation noise in digital Fresnel holography. Especially the anisotropy of the decorrelation noise is established. The second topic presents a new approach for speckle de-noising using deep convolution neural networks. Full Text: PDF ReferencesP. Picart (ed.), New techniques in digital holography (John Wiley & Sons, 2015). CrossRef T.M. Biewer, J.C. Sawyer, C.D. Smith, C.E. Thomas, "Dual laser holography for in situ measurement of plasma facing component erosion (invited)", Rev. Sci. Instr. 89, 10J123 (2018). CrossRef M. Fratz, T. Beckmann, J. Anders, A. Bertz, M. Bayer, T. Gießler, C. Nemeth, D. Carl, "Inline application of digital holography [Invited]", Appl. Opt. 58(34), G120 (2019). CrossRef M.P. Georges, J.-F. Vandenrijt, C. Thizy, Y. Stockman, P. Queeckers, F. Dubois, D. Doyle, "Digital holographic interferometry with CO2 lasers and diffuse illumination applied to large space reflector metrology [Invited]", Appl. Opt. 52(1), A102 (2013). CrossRef E. Meteyer, F. Foucart, M. Secail-Geraud, P. Picart, C. Pezerat, "Full-field force identification with high-speed digital holography", Mech. Syst. Signal Process. 164 (2022). CrossRef L. Lagny, M. Secail-Geraud, J. Le Meur, S. Montresor, K. Heggarty, C. Pezerat, P. Picart, "Visualization of travelling waves propagating in a plate equipped with 2D ABH using wide-field holographic vibrometry", J. Sound Vib. 461 114925 (2019). CrossRef L. Valzania, Y. Zhao, L. Rong, D. Wang, M. Georges, E. Hack, P. Zolliker, "THz coherent lensless imaging", Appl. Opt. 58, G256 (2019). CrossRef V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, P. Ferraro, "Strategies for reducing speckle noise in digital holography", Light: Sci. Appl. 7(1), 1 (2018). CrossRef V. Bianco, P. Memmolo, M. Paturzo, A. Finizio, B. Javidi, P. Ferraro, "Quasi noise-free digital holography", Light. Sci. Appl. 5(9), e16142 (2016). CrossRef R. Horisaki, R. Takagi, J. Tanida, "Deep-learning-generated holography", Appl. Opt. 57(14), 3859 (2018). CrossRef E. Meteyer, F. Foucart, C. Pezerat, P. Picart, "Modeling of speckle decorrelation in digital Fresnel holographic interferometry", Opt. Expr. 29(22), 36180 (2021). CrossRef M. Piniard, B. Sorrente, G. Hug, P. Picart, "Theoretical analysis of surface-shape-induced decorrelation noise in multi-wavelength digital holography", Opt. Expr. 29(10), 14720 (2021). CrossRef P. Picart, S. Montresor, O. Sakharuk, L. Muravsky, "Refocus criterion based on maximization of the coherence factor in digital three-wavelength holographic interferometry", Opt. Lett. 42(2), 275 (2017). CrossRef P. Picart, J. Leval, "General theoretical formulation of image formation in digital Fresnel holography", J. Opt. Soc. Am. A 25, 1744 (2008). CrossRef S. Montresor, P. Picart, "Quantitative appraisal for noise reduction in digital holographic phase imaging", Opt. Expr. 24(13), 14322 (2016). CrossRef S. Montresor, M. Tahon, A. Laurent, P. Picart, "Computational de-noising based on deep learning for phase data in digital holographic interferometry", APL Photonics 5(3), 030802 (2020). CrossRef M. Tahon, S. Montresor, P. Picart, "Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise", Photonics 8(7), 255 (2021). CrossRef E. Meteyer, S. Montresor, F. Foucart, J. Le Meur, K. Heggarty, C. Pezerat, P. Picart, "Lock-in vibration retrieval based on high-speed full-field coherent imaging", Sci. Rep. 11(1), 1 (2021). CrossRef

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Cocianu, Cătălina, and Alexandru Stan. "Neural Architectures for Correlated Noise Removal in Image Processing." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/6153749.

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The paper proposes a new method that combines the decorrelation and shrinkage techniques to neural network-based approaches for noise removal purposes. The images are represented as sequences of equal sized blocks, each block being distorted by a stationary statistical correlated noise. Some significant amount of the induced noise in the blocks is removed in a preprocessing step, using a decorrelation method combined with a standard shrinkage-based technique. The preprocessing step provides for each initial image a sequence of blocks that are further compressed at a certain rate, each component of the resulting sequence being supplied as inputs to a feed-forward neural architectureFX→FH→FY. The local memories of the neurons of the layersFHandFYare generated through a supervised learning process based on the compressed versions of blocks of the same index value supplied as inputs and the compressed versions of them resulting as the mean of their preprocessed versions. Finally, using the standard decompression technique, the sequence of the decompressed blocks is the cleaned representation of the initial image. The performance of the proposed method is evaluated by a long series of tests, the results being very encouraging as compared to similar developments for noise removal purposes.

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Korchynskyi, Volodymyr, Valerii Hordiichuk, Vitalii Kildishev, Oleksandr Riabukha, Sergii Staikutsa, and Khaled Alfaiomi. "Method of information protection based on the integration of probabilistic encryption and noise immune coding." Radioelectronic and Computer Systems, no. 4 (December 6, 2023): 184–95. http://dx.doi.org/10.32620/reks.2023.4.13.

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The subject matter of this article is the processes of increasing information security in communication systems to solve applied problems that are critical in the transmission of confidential data. The goal is to develop a multi-stage method for protecting information from unauthorized access, based on probabilistic encryption integration, error-correcting code, and error decorrelation. The tasks to be solved are as follows: to develop a probabilistic encryption algorithm that considers the entropy of the characters’ appearance in a message; to implement iterative coding with variable parameters of the information bits matrix; and to implement error decorrelation based on the generated matrix by interleaving information and check bits according to a given algorithm. Methodology: theory of signal communication and signal processing, theory of cryptography, theory of noise-resistant codes, probability theory, statistical analysis. The following results were obtained: a multi-stage method for protecting information from unauthorized access was proposed, in which at each step of data conversion, information secrecy is increased; probabilistic encryption was implemented, which considers the entropy of a discrete source of information when forming a space of random combinations; based on an iterative code with specified matrix parameters, an additional increase in the space of random combinations was implemented; error decorrelation reduces the multiplicity of errors in codewords and ensures mixing of bits according to a predetermined law. Conclusions. The scientific novelty of the results obtained is as follows: 1) an improved method of information protection from unauthorized access is proposed, based on probabilistic encryption integration, interference-resistant coding, and error decorrelation; 2) increasing information concealment is realized by eliminating the main shortcomings of probabilistic encryption, which did not take into account the entropy of the symbols of the open message; 3) it is proposed to form the space of random combinations taking into account the average probability of the appearance of a symbol in open texts, which will allow the output of a probabilistic cipher to form a stream of random combinations, which, according to its statistical properties, is close to the uniform distribution law; 4) further development of information protection methods based on interference-resistant coding and error decorrelation with encryption functions is received; 5) a further development of the statistical encryption method is the use of all redundant elements for both probabilistic encryption and interference-resistant coding.

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Schaan, Emmanuel, and Martin White. "Multi-tracer intensity mapping: cross-correlations, line noise & decorrelation." Journal of Cosmology and Astroparticle Physics 2021, no. 05 (May 1, 2021): 068. http://dx.doi.org/10.1088/1475-7516/2021/05/068.

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Fallon, Susan M., and Donald E. Robinson. "The effects of decorrelation on the discriminability of noise samples." Journal of the Acoustical Society of America 81, S1 (May 1987): S33. http://dx.doi.org/10.1121/1.2024195.

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Rao, Qiang, and Jianjun Wang. "Suppressing Random Artifacts in Reference Sensor Pattern Noise via Decorrelation." IEEE Signal Processing Letters 24, no. 6 (June 2017): 809–13. http://dx.doi.org/10.1109/lsp.2017.2681426.

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Vahala, Kerry J., and Michael A. Newkirk. "Intensity noise reduction in semiconductor lasers by amplitude‐phase decorrelation." Applied Physics Letters 57, no. 10 (September 3, 1990): 974–76. http://dx.doi.org/10.1063/1.103530.

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Sivaramakrishnan, Anand, James P. Lloyd, Philip E. Hodge, and Bruce A. Macintosh. "Speckle Decorrelation and Dynamic Range in Speckle Noise–limited Imaging." Astrophysical Journal 581, no. 1 (December 10, 2002): L59—L62. http://dx.doi.org/10.1086/345826.

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Uribe-Patarroyo, Néstor, Anouk L. Post, Sebastián Ruiz-Lopera, Dirk J. Faber, and Brett E. Bouma. "Noise and bias in optical coherence tomography intensity signal decorrelation." OSA Continuum 3, no. 4 (March 17, 2020): 709. http://dx.doi.org/10.1364/osac.385431.

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Duan, Meng, Bing Xu, Zhiwei Li, Wenhao Wu, Yunmeng Cao, Jihong Liu, Guanya Wang, and Jingxin Hou. "A New Weighting Method by Considering the Physical Characteristics of Atmospheric Turbulence and Decorrelation Noise in SBAS-InSAR." Remote Sensing 12, no. 16 (August 9, 2020): 2557. http://dx.doi.org/10.3390/rs12162557.

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Time series of ground subsidence can not only be used to describe motion produced by various anthropocentric and natural process but also to better understand the processes and mechanisms of geohazards and to formulate effective protective measures. For high-accuracy measurement of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), atmospheric turbulence and decorrelation noise are regarded as random variables and cannot be accurately estimated by a deterministic model when large spatio-temporal variability presents itself. Various weighting methods have been proposed and improved continuously to reduce the effects of these two parts and provide uncertainty information of the estimated parameters, simultaneously. Network-based variance-covariance estimation (NVCE) and graph theory (GT) are the two main weighting methods which were developed on the basis of previous algorithms. However, the NVCE weighting method only focuses on the influence of atmospheric turbulence and neglects the decorrelation noise. The GT method weights each interferogram in a time series by using the Laplace transformation. Although simple to implement, it is not reasonable to have an equal weight for each pixel in the same interferogram. To avoid these limitations, this study presents a new weighting method by considering the physical characteristics of atmospheric turbulence and decorrelation noise in SBAS-InSAR images. The effectiveness of the proposed method was tested and validated by using a set of simulated experiments and a case study on a Hawaiian island. According to the GPS-derived displacements, the average RMSE of the results from the new weighting method was 1.66 cm, indicating about an 8% improvement compared with 1.79, 1.80 and 1.80 cm from the unweighted method, the NVCE method and the GT method, respectively.

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Jiang, Yong, Ya Ping Wang, and Dong Mei Zhao. "Self-Adaptive Wavelet Based on Parametric Equation in Manufacturing Engineering." Advanced Materials Research 323 (August 2011): 176–80. http://dx.doi.org/10.4028/www.scientific.net/amr.323.176.

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The paper analyzes the difference between useful signal and noise signal in dissemination characteristic inside wavelet space in manufacturing engineering, and then puts forward a search algorithm based on wavelet decorrelation white noise testing and involved with the parameters in a parametric equation. The algorithm can select wavelet transform to realize the best noise reduction effect in a self-adaptive way according to the characteristic of signal containing noise and signal to noise ratio. At last, simulation experiment and engineering application are made, and their results are compared with the decomposition result of Daubechies wavelet. It’s concluded that self-adaptive wavelet basis can more adequately separate useful information from signal.

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Lauritzen, K. C., S. H. Talisa, and M. Peckerar. "Impact of Decorrelation Techniques on Sampling Noise in Radio-Frequency Applications." IEEE Transactions on Instrumentation and Measurement 59, no. 9 (September 2010): 2272–79. http://dx.doi.org/10.1109/tim.2009.2036344.

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Molimard, Jérôme, Raul Cordero, and Alain Vautrin. "Signal-to-noise based local decorrelation compensation for speckle interferometry applications." Applied Optics 47, no. 19 (June 27, 2008): 3535. http://dx.doi.org/10.1364/ao.47.003535.

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Awad, Samer I., and Jesse T. Yen. "3D Strain Imaging Using a Rectilinear 2D Array." Ultrasonic Imaging 29, no. 4 (October 2007): 220–30. http://dx.doi.org/10.1177/016173460702900403.

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Under mechanical compression, tissue movements are inherently three-dimensional. 2-D strain imaging can suffer from decorrelation noise caused by out-of-plane tissue movement in elevation. With 3-D strain imaging, all tissue movements can be estimated and compensated, hence minimizing out-of-plane decorrelation noise. Promising 3-D strain imaging results have been shown using 1 -D arrays with mechanical translation in elevation. However, the relatively large slice thickness and mechanical translation can degrade image quality. Using 2-D arrays, an improved elevational resolution can be achieved with electronic focusing. Furthermore, scanning with 2-D arrays is also done electronically, which eliminates the need for mechanical translation. In this paper, we demonstrate the feasibility of 3-D strain imaging using a 4 cm × 4 cm ultrasonic sparse rectilinear 2-D array operating at 5MHz. The signal processing combinations of 2-D or 3-D beamforming followed by 2-D or 3-D strain imaging are studied and compared to each other to evaluate the performance of our 3-D strain imaging system. 3-D beamforming followed by 3-D strain imaging showed best performance in all experiments.

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Poittevin, Julien, Pascal Picart, François Gautier, and Charles Pezerat. "Quality assessment of combined quantization-shot-noise-induced decorrelation noise in high-speed digital holographic metrology." Optics Express 23, no. 24 (November 18, 2015): 30917. http://dx.doi.org/10.1364/oe.23.030917.

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Righetti, Raffaella, Jonathan Ophir, Brian S. Garra, Rajah M. Chandrasekhar, and Thomas A. Krouskop. "A New Method for Generating Poroelastograms in Noisy Environments." Ultrasonic Imaging 27, no. 4 (October 2005): 201–20. http://dx.doi.org/10.1177/016173460502700401.

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Poroelastography has been recently introduced as a new elastographic technique that may be used to describe the spatial and temporal behavior of poroelastic materials. The experimental methodology proposed thus far for phantoms and tissues in vitro requires the acquisition of a precompression rf frame, the application of a unit step strain compression to the sample and the acquisition of subsequent post-compression frames from the material. Elastograms and poroelastograms are generated by cross-correlating the sequentially-acquired postcompression frames with the reference precompression frame. The application of poroelastography to tissues in vivo must address the echo decorrelation problems that are encountered due to uncontrolled tissue motion, which may become significant shortly after the acquisition of the precompression frame. In this paper, we investigate the feasibility of performing poroelastography experiments using an alternative experimental scheme. In the proposed experimental methodology, the reference precompression frame is continuously moved while the time interval between the frames that are correlated is kept short. This allows long data acquisition times with simultaneous minimization of the decorrelation due to undesired tissue motion in vivo. We validated this new method using both a step and a ramp compression functions. We performed poroelastographic simulations and experiments in phantoms and in tissues in vivo. The results were compared to those obtained using the traditional acquisition methodology. This study shows that the two methods yield similar results in vitro and suggests that the new method may be more robust to decorrelation noise in applications in vivo.

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Tripp, Bryan P. "Decorrelation of Spiking Variability and Improved Information Transfer Through Feedforward Divisive Normalization." Neural Computation 24, no. 4 (April 2012): 867–94. http://dx.doi.org/10.1162/neco_a_00255.

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Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.

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MATSUMOTO, Mitsuharu, and Shuji HASHIMOTO. "Estimation of Optimal Parameter in ε-Filter Based on Signal-Noise Decorrelation." IEICE Transactions on Information and Systems E92-D, no. 6 (2009): 1312–15. http://dx.doi.org/10.1587/transinf.e92.d.1312.

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Newkirk, M. A., and K. J. Vahala. "Amplitude-phase decorrelation: a method for reducing intensity noise in semiconductor lasers." IEEE Journal of Quantum Electronics 27, no. 1 (1991): 13–22. http://dx.doi.org/10.1109/3.73536.

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Goutcher, R., L. M. O'Kane, and P. B. Hibbard. "Effects of inter-ocular contrast difference and decorrelation noise on disparity discrimination." Journal of Vision 9, no. 8 (March 22, 2010): 281. http://dx.doi.org/10.1167/9.8.281.

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He-Young Jung and Soo-Young Lee. "On the temporal decorrelation of feature parameters for noise-robust speech recognition." IEEE Transactions on Speech and Audio Processing 8, no. 4 (July 2000): 407–16. http://dx.doi.org/10.1109/89.848222.

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Bendoumia, Rédha, Mohammed Kerkar, and Sohib Abderrahim Bouzekkar. "Acoustic noise reduction by new sub-band forward symmetric adaptive decorrelation algorithms." Applied Acoustics 152 (September 2019): 118–26. http://dx.doi.org/10.1016/j.apacoust.2019.03.030.

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ZHANG, Lifu, Xuhui LU, Yi CEN, and Xuejian SUN. "Optimized spatial and spectral decorrelation method for noise estimation in hyperspectral images." National Remote Sensing Bulletin 25, no. 7 (2021): 1411–21. http://dx.doi.org/10.11834/jrs.20219043.

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Zhu, Qi, Yuxuan Ding, Dawei Tu, Haiyan Zhang, and Yue Peng. "Experimental Study of Defect Localization in a Cross-Ply Fiber Reinforced Composite with Diffuse Ultrasonic Waves." Applied Sciences 9, no. 11 (June 6, 2019): 2334. http://dx.doi.org/10.3390/app9112334.

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Diffuse wave inspection benefits from multiple scattering and is suitable for the nondestructive testing of complex structures with high sensitivity. This paper aims to localize the defect in a cross-ply carbon fiber reinforced polymer composite with the diffuse wave field experimentally based on the Locadiff technique. Firstly, the wave diffusivity and dissipation parameters are determined from the diffuse waveforms. Great dissipation is found for this composite plate due to its strong viscoelasticity, which makes the amplitude attenuate fast in a short propagation distance. The signal-to-noise ratios degrade significantly at off-axis directions so that only measurements along the X and Y axes are chosen. Secondly, the decorrelation coefficients are determined using the stretching technique. The decorrelation coefficients decrease initially due to the interaction between the wave fields and the defect and subsequently increase due to the low signal-to-noise ratio at the later time. Based on these data, a sensitivity time domain is chosen to center at t = 50 μs. Together with the defect sensitivity kernel calculated under constant diffusion property assumption, the defect is localized at [270 mm, 265 mm] compared to [300 mm, 280 mm] in the final reference state. This method is promising for early damage detection in fiber reinforced composite structures.

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Cassé, Gibert, Edouart, Chomette, and Crevoisier. "Optical Energy Variability Induced by Speckle: The Cases of MERLIN and CHARM-F IPDA Lidar." Atmosphere 10, no. 9 (September 11, 2019): 540. http://dx.doi.org/10.3390/atmos10090540.

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In the context of the FrenchGerman space lidar mission MERLIN (MEthane Remote LIdar missioN) dedicated to the determination of the atmospheric methane content, an end-to-end mission simulator is being developed. In order to check whether the instrument design meets the performance requirements, simulations have to count all the sources of noise on the measurements like the optical energy variability induced by speckle. Speckle is due to interference as the lidar beam is quasi monochromatic. Speckle contribution to the error budget has to be estimated but also simulated. In this paper, the speckle theory is revisited and applied to MERLIN lidar and also to the DLR (Deutsches Zentrum für Luft und Raumfahrt) demonstrator lidar CHARM-F. Results show: on the signal path, speckle noise depends mainly on the size of the illuminated area on ground; on the solar flux, speckle is fully negligible both because of the pixel size and the optical filter spectral width; on the energy monitoring path a decorrelation mechanism is needed to reduce speckle noise on averaged data. Speckle noises for MERLIN and CHARM-F can be simulated by Gaussian noises with only one random draw by shot separately for energy monitoring and signal paths.

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Qi, Yang, Yu Wang, Jun Hong, and Shaoyan Du. "Additional Reference Height Error Analysis for Baseline Calibration Based on a Distributed Target DEM in TwinSAR-L." Remote Sensing 13, no. 14 (July 13, 2021): 2750. http://dx.doi.org/10.3390/rs13142750.

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In this paper, additional reference height errors, caused by the penetration depth and Signal to Noise Ratio (SNR) decorrelation in desert regions in L-band spaceborne bistatic interferemetric SAR, will introduce significant errors in nowadays baseline calibration method based on distributed target and consequent DEM products. To quantify these two errrors, this paper takes the TwinSAR-L mission as an example, gives an introduction of TwinSAR-L, outlines the theoretical baseline accuracy requirements that need to be satisfied in the TwinSAR-L mission and addresses the additional reference height errors caused by the penetration depth and SNR decorrelation in desert regions in general by taking the TwinSAR-L mission as an example. Based on ALOS-2 data from a dry desert region in the east of Xing Jiang, this paper quantitatively analyzes these additional reference height errors. The results show that the additional reference height errors resulted from the penetration depth and the SNR decorrelation are 1.295 m and 1.39 m, respectively, which would even cause 6.4 mm and 8.6 mm baseline calibration errors. These errors would seriously degrade the baseline calibration accuracy and the consequent DEM product quality. Therefore, our analysis is of great significance not only for baseline calibration, but also for high-quality DEM’s generation, accuracy assessment and geophysical parameters’ quantitative inversion and application.

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Brand, Andreas, Christian Noss, Christian Dinkel, and Markus Holzner. "High-Resolution Measurements of Turbulent Flow Close to the Sediment–Water Interface Using a Bistatic Acoustic Profiler." Journal of Atmospheric and Oceanic Technology 33, no. 4 (April 2016): 769–88. http://dx.doi.org/10.1175/jtech-d-15-0152.1.

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AbstractVelocity profile measurements at high spatial and temporal resolution are required for the detailed study of solute and momentum transfer close to the sediment–water interface. Still, not many devices allow such measurements in natural systems. Recently, a bistatic acoustic current profiler has become commercially available that allows the recording of profiles at down to 1-mm resolution with a maximum frequency of 100 Hz and a profile length of 3.5 cm. This study tested the ability to characterize the turbulent flow of this profiler in a laboratory flume and in a run of the river reservoir. The tests showed that average velocities were reliably measured in the upper 2.5 cm, while the flow statistics were affected by Doppler noise and signal decorrelation. The latter is caused by the decreasing overlap between the individual beam signals. Doppler noise can be estimated and accounted for by established correction procedures, but currently there is no method to quantify the influence of signal decorrelation. Both error sources mainly affect the measured variances of the velocities, while the Reynolds stresses are reliable as long as there is no interference with the solid bottom. In the field application, most problems arise because of the necessity of coordinate system rotation, since a perfect alignment of the profiler with the current is not possible. Also, because of the coordinate system rotation, the Reynolds stresses become contaminated by noise, which can be removed by low-pass filtering. Still, this filtering results in loss of the turbulent signal, which was estimated in this study to be between 2% and 10%.

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Nardelli, Bruno Buongiorno. "A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity." Journal of Atmospheric and Oceanic Technology 29, no. 6 (June 1, 2012): 867–79. http://dx.doi.org/10.1175/jtech-d-11-00099.1.

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Abstract A novel technique for the high-resolution interpolation of in situ sea surface salinity (SSS) observations is developed and tested. The method is based on an optimal interpolation (OI) algorithm that includes satellite sea surface temperature (SST) in the covariance estimation. The covariance function parameters (i.e., spatial, temporal, and thermal decorrelation scales) and the noise-to-signal ratio are determined empirically, by minimizing the root-mean-square error and mean error with respect to fully independent validation datasets. Both in situ observations and simulated data extracted from a numerical model output are used to run these tests. Different filters are applied to sea surface temperature data in order to remove the large-scale variability associated with air–sea interaction, because a high correlation between SST and SSS is expected only at small scales. In the tests performed on in situ observations, the lowest errors are obtained by selecting covariance decorrelation scales of 400 km, 6 days, and 2.75°C, respectively, a noise-to-signal ratio of 0.01 and filtering the scales longer than 1000 km in the SST time series. This results in a root-mean-square error of ~0.11 g kg−1 and a mean error of ~0.01 g kg−1, that is, reducing the errors by ~25% and ~60%, respectively, with respect to the first guess.

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Piniard, Matthieu, Béatrice Sorrente, Gilles Hug, and Pascal Picart. "Theoretical analysis of surface-shape-induced decorrelation noise in multi-wavelength digital holography." Optics Express 29, no. 10 (April 28, 2021): 14720. http://dx.doi.org/10.1364/oe.423391.

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Kobayashi, Masaki, Yasunori Nagasaka, and Yoshio Itoh. "A Noise Free Adaptive Transversal Notch Filter Using SSCF Algorithm with Decorrelation Parameter." IEEJ Transactions on Electronics, Information and Systems 133, no. 12 (2013): 2236–42. http://dx.doi.org/10.1541/ieejeiss.133.2236.

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Xia, Haiting, Silvio Montresor, Rongxin Guo, Junchang Li, Feng Yan, Heming Cheng, and Pascal Picart. "Phase calibration unwrapping algorithm for phase data corrupted by strong decorrelation speckle noise." Optics Express 24, no. 25 (December 5, 2016): 28713. http://dx.doi.org/10.1364/oe.24.028713.

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Stähler, Simon C., and Karin Sigloch. "Fully probabilistic seismic source inversion – Part 2: Modelling errors and station covariances." Solid Earth 7, no. 6 (November 7, 2016): 1521–36. http://dx.doi.org/10.5194/se-7-1521-2016.

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Abstract. Seismic source inversion, a central task in seismology, is concerned with the estimation of earthquake source parameters and their uncertainties. Estimating uncertainties is particularly challenging because source inversion is a non-linear problem. In a companion paper, Stähler and Sigloch (2014) developed a method of fully Bayesian inference for source parameters, based on measurements of waveform cross-correlation between broadband, teleseismic body-wave observations and their modelled counterparts. This approach yields not only depth and moment tensor estimates but also source time functions. A prerequisite for Bayesian inference is the proper characterisation of the noise afflicting the measurements, a problem we address here. We show that, for realistic broadband body-wave seismograms, the systematic error due to an incomplete physical model affects waveform misfits more strongly than random, ambient background noise. In this situation, the waveform cross-correlation coefficient CC, or rather its decorrelation D = 1 − CC, performs more robustly as a misfit criterion than ℓp norms, more commonly used as sample-by-sample measures of misfit based on distances between individual time samples. From a set of over 900 user-supervised, deterministic earthquake source solutions treated as a quality-controlled reference, we derive the noise distribution on signal decorrelation D = 1 − CC of the broadband seismogram fits between observed and modelled waveforms. The noise on D is found to approximately follow a log-normal distribution, a fortunate fact that readily accommodates the formulation of an empirical likelihood function for D for our multivariate problem. The first and second moments of this multivariate distribution are shown to depend mostly on the signal-to-noise ratio (SNR) of the CC measurements and on the back-azimuthal distances of seismic stations. By identifying and quantifying this likelihood function, we make D and thus waveform cross-correlation measurements usable for fully probabilistic sampling strategies, in source inversion and related applications such as seismic tomography.

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Peng, Hui, Juhong Tie, and Dequan Guo. "An Iterative Axial and Lateral Ultrasound Strain Estimator Using Subband Division." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1057–68. http://dx.doi.org/10.1166/jmihi.2020.3024.

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Conventional ultrasound strain imaging usually only calculates the axial strain. Although axial strain is the main component of two dimensional strain field, lateral displacement and strain estimation can provide additional information of human mechanical properties. Shear strain and Poisson’s ratio can be estimated by using lateral strain estimation technique. Low lateral sampling rate and decorrelation noise of lateral radio frequency (RF) signal caused by axial displacement motion increase the difficulty of lateral strain estimation. Subband division technique is to divide a broadband signal into several narrowband signals. In this paper, the application of subband division technique in axial and lateral strain estimation is studied, and an iterative method for estimating axial and lateral strains is proposed based on subband technique. The subband division of this method is carried out along the axial direction, so that the bandwidth of the lateral subband signal is maintained and the quality of the lateral sub strain image is not reduced. In this paper, the number of subbands is three; the compounded lateral strain image is obtained by superimposing these sub strain images on the average. In each iteration, the temporal stretching technique is used to align the axial and lateral RF signals by using the axial and lateral displacement estimation information, which reduces the decorrelation noise of the RF signals. The length of temporal stretching window decreases with the number of iterations, so as to gradually improve the accuracy of temporal stretching. The phase zero algorithm is used to estimate the axial and lateral displacements. The effectiveness of this method is tested by simulations. The simulation results show that the elastographic signal-to-noise ratio (SNRe) of lateral strain image is increased by about 50%, the elastographic contrast noise ratio (CNRe) of lateral strain image is increased by about 120%, the SNRe of axial strain image is increased by about 4%, the CNRe of axial strain image is increased by 8%, and the signal-to-noise ratio of Poisson’s ratio image is increased by about 40%.

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Siegel, Jared C., Ryan A. Rubenzahl, Samuel Halverson, and Andrew W. Howard. "Into the Depths: A New Activity Metric for High-precision Radial Velocity Measurements Based on Line Depth Variations." Astronomical Journal 163, no. 6 (May 11, 2022): 260. http://dx.doi.org/10.3847/1538-3881/ac609a.

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Abstract The discovery and characterization of extrasolar planets using radial velocity (RV) measurements is limited by noise sources from the surfaces of host stars. Current techniques to suppress stellar magnetic activity rely on decorrelation using an activity indicator (e.g., strength of the Ca ii lines, width of the cross-correlation function, broadband photometry) or measurement of the RVs using only a subset of spectral lines that have been shown to be insensitive to activity. Here, we combine the above techniques by constructing a high-signal-to-noise activity indicator, the depth metric  ( t ) , from the most activity-sensitive spectral lines using the “line-by-line” method of Dumusque (2018). Analogous to photometric decorrelation of RVs or Gaussian progress regression modeling of activity indices, time series modeling of  ( t ) reduces the amplitude of magnetic activity in RV measurements; in an αCenB RV time series from HARPS, the RV rms was reduced from 2.67 to 1.02 m s−1.  ( t ) modeling enabled us to characterize injected planetary signals as small as 1 m s−1. In terms of noise reduction and injected signal recovery,  ( t ) modeling outperforms activity mitigation via the selection of activity-insensitive spectral lines. For Sun-like stars with activity signals on the m s−1 level, the depth metric independently tracks rotationally modulated and multiyear stellar activity with a level of quality similar to that of the FWHM of the CCF and log R HK ′ . The depth metric and its elaborations will be a powerful tool in the mitigation of stellar magnetic activity, particularly as a means of connecting stellar activity to physical processes within host stars.

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FLANDRIN, PATRICK, and PAULO GONÇALVÈS. "EMPIRICAL MODE DECOMPOSITIONS AS DATA-DRIVEN WAVELET-LIKE EXPANSIONS." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 04 (December 2004): 477–96. http://dx.doi.org/10.1142/s0219691304000561.

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Huang's data-driven technique of Empirical Mode Decomposition (EMD) is applied to the versatile, broadband, model of fractional Gaussian noise (fGn). The experimental spectral analysis and statistical characterization of the obtained modes reveal an equivalent filter bank structure which shares most properties of a wavelet decomposition in the same context, in terms of self-similarity, quasi-decorrelation and variance progression. Furthermore, the spontaneous adaptation of EMD to "natural" dyadic scales is shown, rationalizing the method as an alternative way for estimating the fGn Hurst exponent.

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Mestre-Quereda, Alejandro, Juan M. Lopez-Sanchez, and Jordi J. Mallorqui. "Range Spectral Filtering in SAR Interferometry: Methods and Limitations." Sensors 22, no. 22 (November 10, 2022): 8696. http://dx.doi.org/10.3390/s22228696.

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A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical decorrelation must be compensated by a specific filtering technique known as range filtering, the goal of which is to estimate this spectral displacement and retain only the common parts of the images’ spectra, reducing the noise and improving the quality of the interferograms. Multiple range filters have been proposed in the literature. The most widely used methods are an adaptive filter approach, which estimates the spectral shift directly from the data; a method based on orbital information, which assumes a constant-slope (or flat) terrain; and slope-adaptive algorithms, which consider both orbital information and auxiliary topographic data. Their advantages and limitations are analyzed in this manuscript and, additionally, a new, more refined approach is proposed. Its goal is to enhance the filtering process by automatically adapting the filter to all types of surface variations using a multi-scale strategy. A pair of RADARSAT-2 images that mapped the mountainous area around the Etna volcano (Italy) are used for the study. The results show that filtering accuracy is improved with the new method including the steepest areas and vegetation-covered regions in which the performance of the original methods is limited.

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Qian, Nijia, Guobin Chang, Pavel Ditmar, Jingxiang Gao, and Zhengqiang Wei. "Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products." Remote Sensing 14, no. 12 (June 11, 2022): 2810. http://dx.doi.org/10.3390/rs14122810.

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High-frequency and correlated noise filtering is one of the important preprocessing steps for GRACE level-2 products before calculating mass anomaly. Decorrelation and denoising kernel (DDK) filters are usually considered as such optimal filters to solve this problem. In this work, a sparse DDK filter is proposed. This is achieved by replacing Tikhonov regularization in traditional DDK filters with weighted L1 norm regularization. The proposed sparse DDK filter adopts a time-varying error covariance matrix, while the equivalent signal covariance matrix is adaptively determined by the Gravity Recovery and Climate Experiment (GRACE) monthly solution. The covariance matrix of the sparse DDK filtered solution is also developed from the Bayesian and error-propagation perspectives, respectively. Furthermore, we also compare and discuss the properties of different filters. The proposed sparse DDK has all the advantages of traditional filters, such as time-varying, location inhomogeneity, and anisotropy, etc. In addition, the filtered solution is sparse; that is, some high-degree and high-order terms are strictly zeros. This sparsity is beneficial in the following sense: high-degree and high-order sparsity mean that the dominating noise in high-degree and high-order terms is completely suppressed, at a slight cost that the tiny signals of these terms are also discarded. The Center for Space Research (CSR) GRACE monthly solutions and their error covariance matrices, from January 2004 to December 2010, are used to test the performance of the proposed sparse DDK filter. The results show that the sparse DDK can effectively decorrelate and denoise these data.

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Journal articles on the topic 'Decorrelation noise'

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