Literatura académica sobre el tema "Image quality estimation"
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Artículos de revistas sobre el tema "Image quality estimation"
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Chin, Sin Chee, Chee-Onn Chow, Jeevan Kanesan, and Joon Huang Chuah. "A Study on Distortion Estimation Based on Image Gradients." Sensors 22, no. 2 (2022): 639. http://dx.doi.org/10.3390/s22020639.
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Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.
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Temel, Dogancan, Mohit Prabhushankar, and Ghassan AlRegib. "UNIQUE: Unsupervised Image Quality Estimation." IEEE Signal Processing Letters 23, no. 10 (2016): 1414–18. http://dx.doi.org/10.1109/lsp.2016.2601119.
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Wang, Qiu Yun. "Depth Estimation Based Underwater Image Enhancement." Advanced Materials Research 926-930 (May 2014): 1704–7. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1704.
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According to the image formation model and the nature of underwater images, we find that the effect of the haze and the color distortion seriously pollute the underwater image data, lowing the quality of the underwater images in the visibility and the quality of the data. Hence, aiming to reduce the noise and the haze effect existing in the underwater image and compensate the color distortion, the dark channel prior model is used to enhance the underwater image. We compare the dark channel prior model based image enhancement method to the contrast stretching based method for image enhancement. The experimental results proved that the dark channel prior model has good ability for processing the underwater images. The super performance of the proposed method is demonstrated as well.
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Li, Chao, Mingyang Li, Jie Liu, Yingchang Li, and Qianshi Dai. "Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest." Forests 11, no. 1 (2019): 45. http://dx.doi.org/10.3390/f11010045.
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To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.
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Xu, Ningshan, Dongao Ma, Guoqiang Ren, and Yongmei Huang. "BM-IQE: An Image Quality Evaluator with Block-Matching for Both Real-Life Scenes and Remote Sensing Scenes." Sensors 20, no. 12 (2020): 3472. http://dx.doi.org/10.3390/s20123472.
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Like natural images, remote sensing scene images; of which the quality represents the imaging performance of the remote sensor, also suffer from the degradation caused by imaging system. However, current methods measuring the imaging performance in engineering applications require for particular image patterns and lack generality. Therefore, a more universal approach is demanded to assess the imaging performance of remote sensor without constraints of land cover. Due to the fact that existing general-purpose blind image quality assessment (BIQA) methods cannot obtain satisfying results on remote sensing scene images; in this work, we propose a BIQA model of improved performance for natural images as well as remote sensing scene images namely BM-IQE. We employ a novel block-matching strategy called Structural Similarity Block-Matching (SSIM-BM) to match and group similar image patches. In this way, the potential local information among different patches can get expressed; thus, the validity of natural scene statistics (NSS) feature modeling is enhanced. At the same time, we introduce several features to better characterize and express remote sensing images. The NSS features are extracted from each group and the feature vectors are then fitted to a multivariate Gaussian (MVG) model. This MVG model is therefore used against a reference MVG model learned from a corpus of high-quality natural images to produce a basic quality estimation of each patch (centroid of each group). The further quality estimation of each patch is obtained by weighting averaging of its similar patches’ basic quality estimations. The overall quality score of the test image is then computed through average pooling of the patch estimations. Extensive experiments demonstrate that the proposed BM-IQE method can not only outperforms other BIQA methods on remote sensing scene image datasets but also achieve competitive performance on general-purpose natural image datasets as compared to existing state-of-the-art FR/NR-IQA methods.
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Huang, Penghe, Dongyan Li, and Huimin Zhao. "An Improved Robust Fractal Image Compression Based on M-Estimator." Applied Sciences 12, no. 15 (2022): 7533. http://dx.doi.org/10.3390/app12157533.
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In this paper, a robust fractal image compression method based on M-estimator is presented. The proposed method applies the M-estimator to the parameter estimation in the fractal encoding procedure using Huber and Tukey’s robust statistics. The M-estimation reduces the influence of the outliers and makes the fractal encoding algorithm robust to the noisy image. Meanwhile, the quadtree partitioning approach has been used in the proposed methods to improve the efficiency of the encoding algorithm, and some unnecessary computations are eliminated in the parameter estimation procedures. The experimental results demonstrate that the proposed method is insensitive to the outliers in the noisy corrupted image. The comparative data shows that the proposed method is superior in both the encoding time and the quality of retrieved images over other robust fractal compression algorithms. The proposed algorithm is useful for multimedia and image archiving, low-cost consumption applications and progressive image transmission of live images, and in reducing computing time for fractal image compression.
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Anikeeva, I., and A. Chibunichev. "RANDOM NOISE ASSESSMENT IN AERIAL AND SATELLITE IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 771–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-771-2021.
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Abstract. Random noise in aerial and satellite images is one of the factors, decreasing their quality. The noise level assessment in images is paid not enough attention. The method of numerical estimation of random image noise is considered. The object of the study is the image noise estimating method, based on harmonic analysis. The capability of using this method for aerial and satellite image quality assessment is considered. The results of the algorithm testing on model data and on real satellite images with different terrain surfaces are carried out. The accuracy estimating results for calculating the root-mean-square deviation (RMS) of random image noise by the harmonic analysis method are shown.
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Kim, Sangmin, Daekwan Kim, Kilwoo Chung, and JoonSeo Yim. "Estimation of any fields of lens PSFs for image simulation." Electronic Imaging 2021, no. 7 (2021): 72–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.7.iss-072.
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In a mobile smartphone camera, image quality is more degraded towards the edges of an image sensor due to high CRA (Chief Ray Angle). It is critical to estimate the cause of this effect since image quality is degraded at image periphery from attenuating illuminance and broadening PSF (point spread function). In order to predict image quality from the center to the edge of the camera output, we propose a method to estimate lens PSFs at any particular image field. The method adopts Zernike polynomials to consider lens aberrations while having an arbitrary spatial sampling. Also, it employs estimating a pupil shape in accordance with an optical field. The proposed method has two steps: 1) estimation of a pupil shape and Zernike polynomial coefficients, and 2) generation of a PSF with estimated parameters. The method was experimented with a typical mobile lens to evaluate the performance of the PSF estimation at 0.0F and 0.8F. In addition, Siemens star images were generated with the estimated PSFs to compare resolutions at the center and the edge of an image. The results show that the image of the edge is worse than that of the center in terms of MTF (Modulation Transfer Function), showing the importance of assessing image quality at the edge for pre-evaluation of a mobile camera.
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Du, Juan. "AIVMAF: Automatic Image Quality Estimation Based on Improved VMAF and YOLOv4." Journal of Physics: Conference Series 2289, no. 1 (2022): 012020. http://dx.doi.org/10.1088/1742-6596/2289/1/012020.
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Abstract The current most widely used way of image quality estimation relies heavily on the subjective assessment, while majority of past objective estimation methods are not satisfactory on accuracy. To solve them and realize unsupervised image quality estimation with high precision, this paper creates a linear way with “Proportional Partition” controlled by horizontal and vertical rates of extracted pixel to get best representations of the image with patching, balance the uneven distribution of image quality in each source image, and offer dynamic compatibility to images with high resolution. Besides, it estimates the image quality automatically with a model trained by current best artificial intelligence (AI) algorithm for target detection YOLOv4 with 1000 images random selected from ImageNet2013 database. The proposal also uses the spirit of joint indices from the current widely used method named Video Multimethod Assessment Fusion (VMAF). But we replace its Visual Information Fidelity (VIF) with Visual Saliency-induced Index (VSI) and add VSI to our target function because of VIF’s dependence on subjective assessment, and also for VSI’s better performance surpassing most recent IQA estimators as TOP3 best model in recent world. Besides, contrast masking is also included by objective function for the KL-divergence to simulate the human visual perception better. A creative “Batch Learning” way is found to address patches for less calculation and faster speed. All source images are pretreated with colour space transformation and normalization to improve descriptiveness of images and reduce the redundant points, and a threshold is devised to formulate suppression mechanisms. The proposed solution is tested to be a good image quality assessor in many aspects such as correctness, consistency, linearity, monotonicity and speed, and performs well on even HD images.
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Salokhiddinov, Sherzod, and Seungkyu Lee. "Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus." Applied Sciences 10, no. 23 (2020): 8522. http://dx.doi.org/10.3390/app10238522.
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Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation.
Tesis sobre el tema "Image quality estimation"
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Akinbola, Akintunde A. "Estimation of image quality factors for face recognition." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4308.
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Thesis (M.S.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains vi, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-56).
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Istenič, Klemen. "Underwater image-based 3D reconstruction with quality estimation." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/672199.
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This thesis addresses the development of resources for accurate scaling and uncertainty estimation of image-based 3D models for scientific purposes based on data acquired with monocular or unsynchronized camera systems in difficult-to-access GPS-denied (underwater) environments.
The developed 3D reconstruction framework allows the creation of textured 3D models based on optical and navigation data and is independent of a specific platform, camera or mission. The dissertation presents two new methods for automatically scaling of SfM-based 3D models using laser scalers. Both were used to perform an in-depth scale error analysis of large-scale models of deep-sea underwater environments to determine the advantages and limitations of image-based 3D reconstruction strategies.
In addition, a novel SfM-based system is proposed to demonstrate the feasibility of producing a globally consistent reconstruction with its uncertainty while the robot is still in the water or shortly after<br>Aquesta tesi aborda el desenvolupament de mètodes per a l'estimació precisa de l’escala i la incertesa de models 3D basats en imatges adquirides amb sistemes de càmeres monoculars o no sincronitzades en entorns submarins, de difícil accés i sense GPS.
El sistema desenvolupat permet la creació de models 3D amb textura fent servir dades òptiques i de navegació, i és independent d’una plataforma, càmera o missió específica. La tesi presenta dos nous mètodes per a l’escalat automàtic de models 3D basats en SfM mitjançant mesuradors làser. Tots dos es van utilitzar per realitzar una anàlisi exhaustiva d'errors d’escalat de models en aigües submarines profundes per determinar avantatges i limitacions de les estratègies de reconstrucció 3D.
A més, es proposa un nou sistema basat en SfM per demostrar la viabilitat de la reconstrucció 3D, globalment consistent, i amb informació d'incertesa mentre el robot encara està a l’aigua o poc després<br>Esta tesis aborda el desarrollo de recursos para el escalado preciso y la estimación de la incertidumbre de modelos 3D basados en imágenes, y con fines científicos.
El marco de reconstrucción 3D desarrollado permite la creación de modelos 3D texturizados basados en datos ópticos y de navegación, adquiridos con sistemas monoculares o no sincronizados de cámaras en entornos (submarinos) de difícil acceso sin disponibilidad de GPS. Además, presenta dos nuevos métodos para el escalado automático de modelos 3D basados en SfM mediante medidores laser. Ambos se utilizaron para analizar los errores en escala, de modelos de ambientes submarinos en aguas profundas, con el fin de determinar las ventajas y las limitaciones de las estrategias de reconstrucción 3D.
Además, se propone un nuevo sistema para demostrar la viabilidad de una reconstrucción global consistente junto con su incertidumbre mientras el robot aún está en el agua o poco después<br>Programa de Doctorat en Tecnologia
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Cui, Lei. "Topics in image recovery and image quality assessment /Cui Lei." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/368.
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Image recovery, especially image denoising and deblurring is widely studied during the last decades. Variational models can well preserve edges of images while restoring images from noise and blur. Some variational models are non-convex. For the moment, the methods for non-convex optimization are limited. This thesis finds new non-convex optimizing method called difference of convex algorithm (DCA) for solving different variational models for various kinds of noise removal problems. For imaging system, noise appeared in images can show different kinds of distribution due to the different imaging environment and imaging technique. Here we show how to apply DCA to Rician noise removal and Cauchy noise removal. The performance of our experiments demonstrates that our proposed non-convex algorithms outperform the existed ones by better PSNR and less computation time. The progress made by our new method can improve the precision of diagnostic technique by reducing Rician noise more efficiently and can improve the synthetic aperture radar imaging precision by reducing Cauchy noise within. When applying variational models to image denoising and deblurring, a significant subject is to choose the regularization parameters. Few methods have been proposed for regularization parameter selection for the moment. The numerical algorithms of existed methods for parameter selection are either complicated or implicit. In order to find a more efficient and easier way to estimate regularization parameters, we create a new image quality sharpness metric called SQ-Index which is based on the theory of Global Phase Coherence. The new metric can be used for estimating parameters for a various of variational models, but also can estimate the noise intensity based on special models. In our experiments, we show the noise estimation performance with this new metric. Moreover, extensive experiments are made for dealing with image denoising and deblurring under different kinds of noise and blur. The numerical results show the robust performance of image restoration by applying our metric to parameter selection for different variational models.
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Thomas, Graham A. "Motion estimation and its application in broadcast television." Thesis, University of Essex, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.258717.
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Tseng, Hsin-Wu, Jiahua Fan, and Matthew A. Kupinski. "Assessing computed tomography image quality for combined detection and estimation tasks." SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2017. http://hdl.handle.net/10150/626451.
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Maintaining or even improving image quality while lowering patient dose is always the desire in clinical computed tomography (CT) imaging. Iterative reconstruction (IR) algorithms have been designed to allow for a reduced dose while maintaining or even improving an image. However, we have previously shown that the dose-saving capabilities allowed with IR are different for different clinical tasks. The channelized scanning linear observer (CSLO) was applied to study clinical tasks that combine detection and estimation when assessing CT image data. The purpose of this work is to illustrate the importance of task complexity when assessing dose savings and to move toward more realistic tasks when performing these types of studies. Human-observer validation of these methods will take place in a future publication. Low-contrast objects embedded in body-size phantoms were imaged multiple times and reconstructed by filtered back projection (FBP) and an IR algorithm. The task was to detect, localize, and estimate the size and contrast of low-contrast objects in the phantom. Independent signal-present and signal-absent regions of interest cropped from images were channelized by the dense-difference of Gauss channels for CSLO training and testing. Estimation receiver operating characteristic (EROC) curves and the areas under EROC curves (EAUC) were calculated by CSLO as the figure of merit. The one-shot method was used to compute the variance of the EAUC values. Results suggest that the IR algorithm studied in this work could efficiently reduce the dose by similar to 50% while maintaining an image quality comparable to conventional FBP reconstruction warranting further investigation using real patient data. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Ghosh, Roy Gourab. "A Simple Second Derivative Based Blur Estimation Technique." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366890068.
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Zhang, Changjun. "Seismic absorption estimation and compensation." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2820.
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As seismic waves travel through the earth, the visco-elasticity of the earth's medium will cause energy dissipation and waveform distortion. This phenomenon is referred to as seismic absorption or attenuation. The absorptive property of a medium can be described by a quality factor Q, which determines the energy decay and a velocity dispersion relationship.
Four new ideas have been developed in this thesis to deal with the estimation and application of seismic absorption. By assuming that the amplitude spectrum of a seismic wavelet may be modeled by that of a Ricker wavelet, an analytical relation has been derived to estimate a quality factor from the seismic data peak frequency variation with time. This relation plays a central role in quality factor estimation problems. To estimate interval Q for reservoir description, a method called reflectivity guided seismic attenuation analysis is proposed. This method first estimates peak frequencies at a common midpoint location, then correlates the peak frequency with sparsely-distributed reflectivities, and finally calculates Q values from the peak frequencies at the reflectivity locations. The peak frequency is estimated from the prestack CMP gather using peak frequency variation with offset analysis which is similar to amplitude variation with offset analysis in implementation. The estimated Q section has the same layer boundaries of the acoustic impedance or other layer properties. Therefore, the seismic attenuation property obtained with the guide of reflectivity is easy to interpret for the purpose of reservoir description. To overcome the instability problem of conventional inverse Q filtering, Q compensation is formulated as a least-squares (LS) inverse problem based on statistical theory. The matrix of forward modeling is composed of time-variant wavelets. The LS de-absorption is solved by an iterative non-parametric approach. To compensate for absorption in migrated seismic sections, a refocusing technique is developed using non-stationary multi-dimensional deconvolution. A numerical method is introduced to calculate the blurring function in layered media, and a least squares inverse scheme is used to remove the blurring effect in order to refocus the migrated image. This refocusing process can be used as an alternative to regular migration with absorption compensation.
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Nezhadarya, Ehsan. "Image derivative estimation and its applications to edge detection, quality monitoring and copyright protection." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44504.
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Multi-order image derivatives are used in many image processing and computer vision applications, such as edge detection, feature extraction, image enhancement, segmentation, matching, watermarking and quality assessment. In some applications, the image derivatives are modified and then inverse-transformed to the image domain. For example, one approach for image denoising is to keep the significant image derivatives and shrink the non-significant derivatives. The denoised image
is then reconstructed from the modified derivatives. The main challenge here is how to inverse-transform the derivatives to the image domain. This thesis proposes different algorithms to estimate the image derivatives and apply them to image denosing , watermarking and quality assessment. For noisy color images, we present a method that yields accurate and robust estimates of the gradient magnitude and direction. This method obtains the gradient at a certain direction by applying a prefilter and a postfilter in the perpendicular direction. Simulation results show that the proposed method outperforms state-of-the-art methods. We also present a multi-scale derivative transform, MSDT, that obtains the
gradient at a given image scale using the detail horizontal, vertical and diagonal wavelet coefficients of the image at that scale. The inverse transform is designed such that any change in the image derivative results in the minimum possible change in the image. The MSDT transform is used to derive a novel multi-scale image watermarking method. This method embeds the watermark bits in the angles of the significant gradient vectors, at different image scales. Experimental results show that the proposed method outperforms other watermarking methods in terms
of robustness to attacks, imperceptibility of the watermark and watermark capacity.The MSDT is then used to obtain a semi-blind method for video quality assessment. The method embeds pseudo-random binary watermarks in the derivative
vectors of the original undistorted video. The quality of the distorted video is estimated based on the similarity between the embedded and the extracted watermarks. The simulation results on video distorted by compression/decompression show that the proposed method can accurately estimate the quality of a video and its frames for a wide range of compression ratios.
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Fuin, N. "Estimation of the image quality in emission tomography : application to optimization of SPECT system design." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1417803/.
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In Emission Tomography the design of the Imaging System has a great influence on the quality of the output image. Optimisation of the system design is a difficult problem due to the computational complexity and to the challenges in its mathematical formulation. In order to compare different system designs, an efficient and effective method to calculate the Image Quality is needed. In this thesis the statistical and deterministic methods for the calculation of the uncertainty in the reconstruction are presented. In the deterministic case, the Fisher Information Matrix (FIM) formalism can be employed to characterize such uncertainty. Unfortunately, computing, storing and inverting the FIM is not feasible with 3D imaging systems. In order to tackle the problem of the computational load in calculating the inverse of the FIM a novel approximation, that relies on a sub-sampling of the FIM, is proposed. The FIM is calculated over a subset of voxels arranged in a grid that covers the whole volume. This formulation reduces the computational complexity in inverting the FIM but nevertheless accounts for the global interdependence between the variables, for the acquisition geometry and for the object dependency. Using this approach, the noise properties as a function of the system geometry parameterisation were investigated for three different cases. In the first study, the design of a parallel-hole collimator for SPECT is optimised. The new method can be applied to evaluating problems like trading-off collimator resolution and sensitivity. In the second study, the reconstructed image quality was evaluated in the case of truncated projection data; showing how the subsampling approach is very accurate for evaluating the effects of missing data. Finally, the noise properties of a D-SPECT system were studied for varying acquisition protocols; showing how the new method is well-suited to problems like optimising adaptive data sampling schemes.
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Al, Chami Zahi. "Estimation de la qualité des données multimedia en temps réel." Thesis, Pau, 2021. http://www.theses.fr/2021PAUU3066.
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Au cours de la dernière décennie, les fournisseurs de données ont généré et diffusé une grande quantité de données, notamment des images, des vidéos, de l'audio, etc. Dans cette thèse, nous nous concentrerons sur le traitement des images puisqu'elles sont les plus communément partagées entre les utilisateurs sur l'inter-réseau mondial. En particulier, le traitement des images contenant des visages a reçu une grande attention en raison de ses nombreuses applications, telles que les applications de divertissement et de médias sociaux. Cependant, plusieurs défis pourraient survenir au cours de la phase de traitement et de transmission : d'une part, le nombre énorme d'images partagées et produites à un rythme rapide nécessite un temps de traitement et de livraison considérable; d’autre part, les images sont soumises à un très grand nombre de distorsions lors du traitement, de la transmission ou de la combinaison de nombreux facteurs qui pourraient endommager le contenu des images. Deux contributions principales sont développées. Tout d'abord, nous présentons un framework d'évaluation de la qualité d'image ayant une référence complète en temps réel, capable de : 1) préserver le contenu des images en s'assurant que certaines informations visuelles utiles peuvent toujours être extraites de l'image résultante, et 2) fournir un moyen de traiter les images en temps réel afin de faire face à l'énorme quantité d'images reçues à un rythme rapide. Le framework décrit ici est limité au traitement des images qui ont accès à leur image de référence (connu sous le nom référence complète). Dans notre second chapitre, nous présentons un framework d'évaluation de la qualité d'image sans référence en temps réel. Il a les capacités suivantes : a) évaluer l'image déformée sans avoir recours à son image originale, b) préserver les informations visuelles les plus utiles dans les images avant de les publier, et c) traiter les images en temps réel, bien que les modèles d'évaluation de la qualité des images sans référence sont considérés très complexes. Notre framework offre plusieurs avantages par rapport aux approches existantes, en particulier : i. il localise la distorsion dans une image afin d'évaluer directement les parties déformées au lieu de traiter l'image entière, ii. il a un compromis acceptable entre la précision de la prédiction de qualité et le temps d’exécution, et iii. il pourrait être utilisé dans plusieurs applications, en particulier celles qui fonctionnent en temps réel. L'architecture de chaque framework est présentée dans les chapitres tout en détaillant les modules et composants du framework. Ensuite, un certain nombre de simulations sont faites pour montrer l'efficacité de nos approches pour résoudre nos défis par rapport aux approches existantes<br>Over the past decade, data providers have been generating and streaming a large amount of data, including images, videos, audio, etc. In this thesis, we will be focusing on processing images since they are the most commonly shared between the users on the global inter-network. In particular, treating images containing faces has received great attention due to its numerous applications, such as entertainment and social media apps. However, several challenges could arise during the processing and transmission phase: firstly, the enormous number of images shared and produced at a rapid pace requires a significant amount of time to be processed and delivered; secondly, images are subject to a wide range of distortions during the processing, transmission, or combination of many factors that could damage the images’content. Two main contributions are developed. First, we introduce a Full-Reference Image Quality Assessment Framework in Real-Time, capable of:1) preserving the images’content by ensuring that some useful visual information can still be extracted from the output, and 2) providing a way to process the images in real-time in order to cope with the huge amount of images that are being received at a rapid pace. The framework described here is limited to processing those images that have access to their reference version (a.k.a Full-Reference). Secondly, we present a No-Reference Image Quality Assessment Framework in Real-Time. It has the following abilities: a) assessing the distorted image without having its distortion-free image, b) preserving the most useful visual information in the images before publishing, and c) processing the images in real-time, even though the No-Reference image quality assessment models are considered very complex. Our framework offers several advantages over the existing approaches, in particular: i. it locates the distortion in an image in order to directly assess the distorted parts instead of processing the whole image, ii. it has an acceptable trade-off between quality prediction accuracy and execution latency, andiii. it could be used in several applications, especially these that work in real-time. The architecture of each framework is presented in the chapters while detailing the modules and components of the framework. Then, a number of simulations are made to show the effectiveness of our approaches to solve our challenges in relation to the existing approaches
Libros sobre el tema "Image quality estimation"
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Jensen, Jørgen Arendt. Medical ultrasound imaging: An estimation based approach. Electronics Laboratory, Technical University of Denmark, 1988.
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Kondo, Kazuhiro. Subjective Quality Measurement of Speech: Its Evaluation, Estimation and Applications. Springer Berlin Heidelberg, 2012.
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Kondo, Kazuhiro. Subjective Quality Measurement of Speech: Its Evaluation, Estimation and Applications. Springer, 2014.
Buscar texto completoCapítulos de libros sobre el tema "Image quality estimation"
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Li, Qin, and Bin Xie. "Image-Based Air Quality Estimation." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_14.
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Aichinger, Horst, Joachim Dierker, Sigrid Joite-Barfuß, and Manfred Säbel. "Patient Dose Estimation." In Radiation Exposure and Image Quality in X-Ray Diagnostic Radiology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11241-6_18.
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Aichinger, Horst, Joachim Dierker, Sigrid Joite-Barfuß, and Manfred Säbel. "Patient Dose Estimation." In Radiation Exposure and Image Quality in X-Ray Diagnostic Radiology. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09654-3_17.
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Liu, Haoting, Jin Yang, Wei Wang, Zhexi He, and Wenpeng Yu. "Illumination Effect Estimation Based on Image Quality Assessment." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48224-7_35.
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Martínez-Usó, Adolfo, Filiberto Pla, and Pedro García-Sevilla. "Multispectral Image Segmentation by Energy Minimization for Fruit Quality Estimation." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_84.
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Zhu, En, Jianping Yin, Chunfeng Hu, and Guomin Zhang. "Quality Estimation of Fingerprint Image Based on Neural Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539117_10.
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Mangulkar, Madhuri N., Suddhasheel Ghosh, and Sanjay S. Jamkar. "Estimation of Aggregate Characteristics Using Digital Image Processing." In ICRRM 2019 – System Reliability, Quality Control, Safety, Maintenance and Management. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8507-0_2.
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Hernandez-Ortega, Javier, Shigenori Nagae, Julian Fierrez, and Aythami Morales. "Quality-Based Pulse Estimation from NIR Face Video with Application to Driver Monitoring." In Pattern Recognition and Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31321-0_10.
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Jaramillo, Miguel A., J. álvaro Fernández, José M. Montanero, and Fernando Zayas. "Image Quality Enhancement for Liquid Bridge Parameter Estimation with DTCNN." In Bio-Inspired Applications of Connectionism. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45723-2_29.
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Wang, Ren-Jie, Yan-Ting Jiang, Jiunn-Tsair Fang, and Pao-Chi Chang. "Quality Estimation for H.264/SVC Inter-layer Residual Prediction in Spatial Scalability." In Advances in Image and Video Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25346-1_23.
Texto completoActas de conferencias sobre el tema "Image quality estimation"
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Efimov, Aleksey I., Dmitry I. Ustukov, and Yevgeniy R. Muratov. "Image Superimposition Quality Estimation Algorithms." In 2020 9th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2020. http://dx.doi.org/10.1109/meco49872.2020.9134163.
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Zhao, Yulan, Chunfeng Jiang, Xiufeng Fang, and Bin Huang. "Research of Fingerprint Image Quality Estimation." In 2009 International Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, 2009. http://dx.doi.org/10.1109/dasc.2009.112.
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Burns, Peter D. "Estimation error in image quality measurements." In IS&T/SPIE Electronic Imaging, edited by Susan P. Farnand and Frans Gaykema. SPIE, 2011. http://dx.doi.org/10.1117/12.872626.
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Loginov, Aleksandr A., Evgeniy R. Muratov, and Mikhail B. Nikiforov. "Image Quality Estimation Using Integral Indicator." In the International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3093241.3093250.
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de Angelis, A., A. Moschitta, F. Russo, and P. Carbone. "Image Quality Assessment: an Overview and some Metrological Considerations." In 2007 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2007. http://dx.doi.org/10.1109/amuem.2007.4362569.
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SanMiguel, Juan C., and Andrea Cavallaro. "Efficient estimation of target detection quality." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296414.
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Yoshida, T., and T. Miyata. "Adaptive frame interval control and its quality estimation." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530306.
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Guan, Jingwei, and Wai-Kuen Cham. "Quality estimation based multi-focus image fusion." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952504.
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Shumilov, Yurij P., Peter A. Bakut, Irina A. Grishina, and Victor V. Sychev. "Segmented primary mirror telescope image quality estimation." In Astronomical Telescopes and Instrumentation, edited by Philippe Dierickx. SPIE, 2000. http://dx.doi.org/10.1117/12.391515.
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Demirtas, A. Murat, Amy R. Reibman, and Hamid Jafarkhani. "Image quality estimation for different spatial resolutions." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738078.
Texto completoInformes sobre el tema "Image quality estimation"
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Saltus, Christina, Molly Reif, and Richard Johansen. waterquality for ArcGIS Pro Toolbox. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42240.
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Monitoring water quality of small inland lakes and reservoirs is a critical component of USACE water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs that cover vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an ESRI ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) that was founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available 1) to automate the download of Sentinel-2 Level-2A imagery, 2) to create stacked image with options for cloud and non-water features masks, 3) to apply water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and 4) to create linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user's guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool's inputs, outputs, and troubleshooting guidance.
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Saltus, Christina, Molly Reif, and Richard Johansen. waterquality for ArcGIS Pro Toolbox : user's guide. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45362.
Texto completoResumen
Monitoring water quality of small inland lakes and reservoirs is a critical component of the US Army Corps of Engineers (USACE) water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available: (1) automating the download of Sentinel-2 Level-2A imagery, (2) creating stacked image with options for cloud and non-water features masks, (3) applying water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and (4) creating linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user’s guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool’s inputs, outputs, and troubleshooting guidance.
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Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7696531.bard.
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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water limited. The objectives of this study were to: (1) determine accuracy in determining GPC of HRSW in Israel and SWWW in Oregon using on-combine optical sensors under field conditions; (2) develop a quantitative relationship between image spectral reflectance and effective crop physiological parameters; (3) develop an operational precision N management procedure that combines variable-rate N recommendations at planting as derived from maps of grain yield, GPC, and test weight; and at mid-season as derived from quantitative relationships, remote sensing, and the DSS; and (4) address the economic and technology-transfer aspects of producers’ needs. Results from the research suggest that optical sensing and the DSS can be used for estimating the N status of dryland wheat and deciding whether additional N is needed to improve GPC. Significant findings include: 1. In-line NIR reflectance spectroscopy can be used to rapidly and accurately (SEP <5.0 mg g⁻¹) measure GPC of a grain stream conveyed by an auger. 2. On-combine NIR spectroscopy can be used to accurately estimate (R² < 0.88) grain test weight across fields. 3. Precision N management based on N removal increases GPC, grain yield, and profitability in rainfed wheat. 4. Hyperspectral SI and partial least squares (PLS) models have excellent potential for estimation of biomass, and water and N contents of wheat. 5. A novel heading index can be used to monitor spike emergence of wheat with classification accuracy between 53 and 83%. 6. Index MCARI/MTVI2 promises to improve remote sensing of wheat N status where water- not soil N fertility, is the main driver of plant growth. Important features include: (a) computable from commercial aerospace imagery that include the red edge waveband, (b) sensitive to Chl and resistant to variation in crop biomass, and (c) accommodates variation in soil reflectance. Findings #1 and #2 above enable growers to further implement an efficient, low cost PNM approach using commercially available on-combine optical sensors. Finding #3 suggests that profit opportunities may exist from PNM based on information from on-combine sensing and aerospace remote sensing. Finding #4, with its emphasis on data retrieval and accuracy, enhances the potential usefulness of a DSS as a tool for field crop management. Finding #5 enables land managers to use a DSS to ascertain at mid-season whether a wheat crop should be harvested for grain or forage. Finding #6a expands potential commercial opportunities of MS imagery and thus has special importance to a majority of aerospace imaging firms specializing in the acquisition and utilization of these data. Finding #6b on index MCARI/MVTI2 has great potential to expand use of ground-based sensing and in-season N management to millions of hectares of land in semiarid environments where water- not N, is the main determinant of grain yield. Finding #6c demonstrates that MCARI/MTVI2 may alleviate the requirement of multiple N-rich reference strips to account for soil differences within farm fields. This simplicity will be less demanding of grower resources, promising substantially greater acceptance of sensing technologies for in-season N management.