Готові списки джерел за темами / Color denoising

Добірка наукової літератури з теми "Color denoising"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Color denoising"

Netravali, Ilka A., Robert J. Holt, and Charles Webb. "Perceptual denoising of color images." International Journal of Imaging Systems and Technology 20, no. 3 (August 16, 2010): 215–22. http://dx.doi.org/10.1002/ima.20240.

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Komatsu, Rina, and Tad Gonsalves. "Comparing U-Net Based Models for Denoising Color Images." AI 1, no. 4 (October 12, 2020): 465–87. http://dx.doi.org/10.3390/ai1040029.

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Анотація:

Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and specific kind of noise using a devoted deep-learning model. Lack of generalization is a major limitation of these models. They cannot be extended to filter image noises other than those for which they are designed. This study deals with the design and training of a generalized deep learning denoising model that can remove five different kinds of noise from any digital image: Gaussian noise, salt-and-pepper noise, clipped whites, clipped blacks, and camera shake. The denoising model is constructed on the standard segmentation U-Net architecture and has three variants—U-Net with Group Normalization, Residual U-Net, and Dense U-Net. The combination of adversarial and L1 norm loss function re-produces sharply denoised images and show performance improvement over the standard U-Net, Denoising Convolutional Neural Network (DnCNN), and Wide Interface Network (WIN5RB) denoising models.

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Thomas, Jency, and Remya S. "PLOW Filter for Color Image Denoising." International Journal of Computer Applications 79, no. 13 (October 18, 2013): 1–7. http://dx.doi.org/10.5120/13798-1855.

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Shen, Yi, Bin Han, and Elena Braverman. "Adaptive frame-based color image denoising." Applied and Computational Harmonic Analysis 41, no. 1 (July 2016): 54–74. http://dx.doi.org/10.1016/j.acha.2015.04.001.

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Lukac, Rastislav, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos. "Color image denoising using evolutionary computation." International Journal of Imaging Systems and Technology 15, no. 5 (2005): 236–51. http://dx.doi.org/10.1002/ima.20058.

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He, Shui Ming, and Xue Lin Li. "Applications of Color Morphology in Image Denoising." Advanced Materials Research 1037 (October 2014): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.393.

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Анотація:

Mathematical morphology can be seen as a special digital image processing method and theory, which has been widely used in various fields. In this paper, the mathematical morphology is applied to the color image processing. In thespace of color image, I have simply expounded the theories and properties of color morphological changes, and defined its morphological operators. According to the application of omni-directional and multi-angle structuring elements composite morphological filter in gray image, I put forward a kind of color morphological filter with omni-directional and multi-angle structuring elements composite. This algorithm has retained its advantages in gray image, however, remaining some drawbacks. Through the optimization of results based on this algorithm, we finally get the relatively ideal denoising effects.Keywords: mathematical morphology;color model;color model; color morphological filter

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Liang, Dong Tai. "Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis." Applied Mechanics and Materials 37-38 (November 2010): 248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.248.

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Inspired by the human vision system, a new image representation and analysis model based on Gaussian multiscale multivariate image analysis (MIA) is proposed. The multiscale color texture representations for the original image are used to constitute the multivariate image, each channel of which represents a perceptual observation from different scales. Then the MIA decomposes this multivariate image into multiscale color texture perceptual features (the principal component score images). These score images could be interpreted as 1) the output of three color opponent channels: black versus white, red versus green and blue versus yellow, and 2) the edge information, and 3) higher-order Gaussian derivatives. Finally the color image denoising approach based on the models is presented. Experiments show that this denoising method against Gaussian filters significantly improves the denoising effect by preserving more edge information.

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Park, Yunjin, Sukho Lee, Byeongseon Jeong, and Jungho Yoon. "Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network." Sensors 20, no. 10 (May 24, 2020): 2970. http://dx.doi.org/10.3390/s20102970.

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Анотація:

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.

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Han, Zhenghao, Li Li, Weiqi Jin, Xia Wang, Gangcheng Jiao, Xuan Liu, and Hailin Wang. "Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS." Sensors 21, no. 11 (June 4, 2021): 3891. http://dx.doi.org/10.3390/s21113891.

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Анотація:

Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image intensifiers. In order to make the ICMOS capable of color imaging in low-light-level conditions, a liquid-crystal tunable filter based color imaging ICMOS was developed. Due to the time-division color imaging scheme, motion artifacts may be introduced when a moving target is in the scene. To solve this problem, a deformable kernel prediction neural network (DKPNN) is proposed for joint denoising and motion artifact removal, and a data generation method which generates images with color-channel motion artifacts is also proposed to train the DKPNN. The results show that, compared with other denoising methods, the proposed DKPNN performed better both on generated noisy data and on real noisy data. Therefore, the proposed DKPNN is more suitable for color ICMOS denoising and motion artifact removal. A new exploration was made for low-light-level color imaging schemes.

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Shamshad, Fahad, M. Mohsin Riaz, and Abdul Ghafoor. "Poisson Denoising for Astronomical Images." Advances in Astronomy 2018 (June 10, 2018): 1–7. http://dx.doi.org/10.1155/2018/2417939.

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Анотація:

A denoising scheme for astronomical color images/videos corrupted with Poisson noise is proposed. The scheme employs the concept of Exponential Principal Component Analysis and sparsity of image patches. The color space RGB is converted to YCbCr and K-means++ clustering is applied on luminance component only. The cluster centers are used for chromatic components to improve the computational efficiency. For videos, the information of both spatial and temporal correlations improves the denoising. Simulation results verify the significance of proposed scheme in both visual and quantitative manner.

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Більше джерел

Дисертації з теми "Color denoising"

Rafi, Nazari Mina. "Denoising and Demosaicking of Color Images." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35802.

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Анотація:

Most digital cameras capture images through Color Filter Arrays (CFA), and reconstruct the full color image from the CFA image. Each CFA pixel only captures one primary color component at each pixel location; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. Some other CFAs contain four color filters. The additional filter is a panchromatic/white filter, and it usually receives the full light spectrum. In this research, we studied and compared different four channel CFAs with panchromatic/white filter, and compared them with three channel CFAs. An appropriate demosaicking algorithm has been developed for each CFA. The most well-known three-channel CFA is Bayer. The Fujifilm X-Trans pattern has been studied in this work as another three-channel CFA with a different structure. Three different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW- $5 \times 5$. The structure and the number of filters for each color are different for these CFAs. Since the Least-Square Luma-Chroma Demultiplexing method is a state of the art demosaicking method for the Bayer CFA, we designed the Least-Square method for RGBW CFAs. The effect of noise on different CFA patterns will be discussed for four channel CFAs. The Kodak database has been used to evaluate our non-adaptive and adaptive demosaicking methods as well as the optimized algorithms with the least square method. The captured values of white (panchromatic/clear) filters in RGBW CFAs have been estimated using red, green and blue filter values. Sets of optimized coefficients have been proposed to estimate the white filter values accurately. The results have been validated using the actual white values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white filters, and the results have been compared. The results in both cases have been compared with the previous works on RGB-Bayer CFA, and it shows that the proposed algorithm using RGBW-Bayer CFA is working better than RGB-Bayer CFA in presence of noise.

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Deng, Hao. "Mathematical approaches to digital color image denoising." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31708.

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Анотація:

Thesis (Ph.D)--Mathematics, Georgia Institute of Technology, 2010.
Committee Chair: Haomin Zhou; Committee Member: Luca Dieci; Committee Member: Ronghua Pan; Committee Member: Sung Ha Kang; Committee Member: Yang Wang. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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IRFAN, MUHAMMAD ABEER. "Joint geometry and color denoising for 3D point clouds." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912976.

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Zhang, Chen. "Poisson Noise Parameter Estimation and Color Image Denoising for Real Camera Hardware." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1575968356242716.

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Åström, Freddie, George Baravdish, and Michael Felsberg. "On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising." Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79603.

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The case when a partial differential equation (PDE) can be considered as an Euler-Lagrange (E-L) equation of an energy functional, consisting of a data term and a smoothness term is investigated. We show the necessary conditions for a PDE to be the E-L equation for a corresponding functional. This energy functional is applied to a color image denoising problem and it is shown that the method compares favorably to current state-of-the-art color image denoising techniques.
NACIP
GARNICS
ELLIIT

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Malek, Mohamed. "Extension de l'analyse multi-résolution aux images couleurs par transformées sur graphes." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2304/document.

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Dans ce manuscrit, nous avons étudié l’extension de l’analyse multi-résolution aux images couleurs par des transformées sur graphe. Dans ce cadre, nous avons déployé trois stratégies d’analyse différentes. En premier lieu, nous avons défini une transformée basée sur l’utilisation d’un graphe perceptuel dans l’analyse à travers la transformé en ondelettes spectrale sur graphe. L’application en débruitage d’image met en évidence l’utilisation du SVH dans l’analyse des images couleurs. La deuxième stratégie consiste à proposer une nouvelle méthode d’inpainting pour des images couleurs. Pour cela, nous avons proposé un schéma de régularisation à travers les coefficients d’ondelettes de la TOSG, l’estimation de la structure manquante se fait par la construction d’un graphe des patchs couleurs à partir des moyenne non locales. Les résultats obtenus sont très encourageants et mettent en évidence l’importance de la prise en compte du SVH. Dans la troisième stratégie, nous proposons une nouvelleapproche de décomposition d’un signal défini sur un graphe complet. Cette méthode est basée sur l’utilisation des propriétés de la matrice laplacienne associée au graphe complet. Dans le contexte des images couleurs, la prise en compte de la dimension couleur est indispensable pour pouvoir identifier les singularités liées à l’image. Cette dernière offre de nouvelles perspectives pour une étude approfondie de son comportement
In our work, we studied the extension of the multi-resolution analysis for color images by using transforms on graphs. In this context, we deployed three different strategies of analysis. Our first approach consists of computing the graph of an image using the psychovisual information and analyzing it by using the spectral graph wavelet transform. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. Results in image restoration highlight the interest of the appropriate use of color information. In the second strategy, we propose a novel recovery algorithm for image inpainting represented in the graph domain. Motivated by the efficiency of the wavelet regularization schemes and the success of the nonlocal means methods we construct an algorithm based on the recovery of information in the graph wavelet domain. At each step the damaged structure are estimated by computing the non local graph then we apply the graph wavelet regularization model using the SGWT coefficient. The results are very encouraging and highlight the use of the perceptual informations. In the last strategy, we propose a new approach of decomposition for signals defined on a complete graphs. This method is based on the exploitation of of the laplacian matrix proprieties of the complete graph. In the context of image processing, the use of the color distance is essential to identify the specificities of the color image. This approach opens new perspectives for an in-depth study of its behavior

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Tang, Hsueh-Yung, and 唐學用. "Color Filter Array Denoising Method for Digital Cameras." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/8pv2r8.

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碩士
國立交通大學
電機學院碩士在職專班電機與控制組
96
Nowadays, traditional film camera has almost been replaced by digital camera in commercial market. This trend is not only on camera, but also on any product in which the signal can be digitalized, since digital information would be much convenience to be processed, stored, and transmitted. Image processing in digital camera consists of many processes. Almost all of the processes would enhance noise added in images. The best way to make noise strength be minimized is to reduce noise in front of any image processing. The difficulty of image denoising is always to preserve edge information, and filters out noise in flat area simultaneously. In this paper, we have presented a denoising method which consists of three ideas. One is to filter noisy pixel based on nearest pattern to keep edge information, another one is to use camera noise characteristic to judge the uniformity of current processed area and the last one is to make use of the property of spatial masking to keep edge information again on the highly texture area.

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Huang, Yi-Sheng, and 黃弌聖. "Color Image Denoising via Sparse and Redundant Representations over Online Dictionary." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/54706257264214133641.

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Анотація:

碩士
國立中正大學
資訊工程研究所
99
An image is easy to get noise by transferring, so image denoising is to address this problem. Image denoising is an ill-posed problem in image processing. In this study, a color image denoising method using sparse and redundant representations over online dictionary is proposed. The proposed image denoising method contains six stages: (1) separating the noisy color image to the RGB components; (2) color decorrelation; (3) Constructing a dictionary which name called “online dictionary” by online dictionary learning algorithm with two images. (4) denoising the three components by online dictionary separately; (5) color correlation; and (6) merging the denoised RGB components to a denoisied color image. Based on experimental results obtained in this study, the subjective or objective measure for the results, the proposed method provides the better image denoising results compared with two comparison methods.

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Yoon, Miun. "Variational and partial differential equation models for color image denoising and their numerical approximations using finite element methods." 2006. http://etd.utk.edu/2006/YoonMiun.pdf.

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(6905153), Omar A. Elgendy. "Image Processing for Quanta Image Sensors." Thesis, 2019.

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Анотація:

Since the birth of charge coupled devices (CCD) and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors, pixel pitch of digital image sensors has been continuously shrinking to meet the resolution and size requirements of the cameras. However, shrinking pixels reduces the maximum number of photons a sensor can hold, a phenomenon broadly known as the full-well capacity limit. The drop in full-well capacity causes drop in signal-to-noise ratio and dynamic range.

The Quanta Image Sensor (QIS) is a class of solid-state image sensors proposed by Eric Fossum in 2005 as a potential solution for the limited full-well capacity problem. QIS is envisioned to be the next generation image sensor after CCD and CMOS since it enables sub-diffraction-limit pixels without the inherited problems of pixel shrinking. Equipped with a massive number of detectors that have single-photon sensitivity, the sensor counts the incoming photons and triggers a binary response “1” if the photon count exceeds a threshold, or “0” otherwise. To acquire an image, the sensor oversamples the space and time to generate a sequence of binary bit maps. Because of this binary sensing mechanism, the full-well capacity, signal-to-noise ratio and the dynamic range can all be improved using an appropriate image reconstruction algorithm. The contribution of this thesis is to address three image processing problems in QIS: 1) Image reconstruction, 2) Threshold design and 3) Color filter array design.

Part 1 of the thesis focuses on reconstructing the latent grayscale image from the QIS binary measurements. Image reconstruction is a necessary step for QIS because the raw binary measurements are not images. Previous methods in the literature use iterative algorithms which are computationally expensive. By modeling the QIS binary measurements as quantized Poisson random variables, a new non-iterative image reconstruction method based on the Transform-Denoise framework is proposed. Experimental results show that the new method produces better quality images while requiring less computing time.

Part 2 of the thesis considers the threshold design problem of a QIS. A spatially-varying threshold can significantly improve the reconstruction quality and the dynamic range. However, no known method of how to achieve this can be found in the literature. The theoretical analysis of this part shows that the optimal threshold should match with the underlying pixel intensity. In addition, the analysis proves the existence of a set of thresholds around the optimal threshold that give asymptotically unbiased reconstructions. The asymptotic unbiasedness has a phase transition behavior. A new threshold update scheme based on this idea is proposed. Experimentally, the new method can provide good estimates of the thresholds with less computing budget compared to existing methods.

Part 3 of the thesis extends QIS capabilities to color imaging by studying how a color filter array should be designed. Because of the small pixel pitch of QIS, crosstalk between neighboring pixels is inevitable and should be considered when designing the color filter arrays. However, optimizing the light efficiency while suppressing aliasing and crosstalk in a color filter array are conflicting tasks. A new optimization framework is proposed to solve the problem. The new framework unifies several mainstream design criteria while offering generality and flexibility. Extensive experimental comparisons demonstrate the effectiveness of the framework.

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Частини книг з теми "Color denoising"

Zhang, Jian-jun, Jian-li Zhang, and Meng Gao. "Two Effective Algorithms for Color Image Denoising." In Lecture Notes in Computer Science, 207–17. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68345-4_19.

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Lukin, Vladimir, Sergey Abramov, Ruslan Kozhemiakin, Alexey Rubel, Mikhail Uss, Nikolay Ponomarenko, Victoriya Abramova, et al. "DCT-Based Color Image Denoising: Efficiency Analysis and Prediction." In Color Image and Video Enhancement, 55–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09363-5_3.

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Bosco, Angelo, Sebastiano Battiato, Arcangelo Bruna, and Rosetta Rizzo. "Texture Sensitive Denoising for Single Sensor Color Imaging Devices." In Lecture Notes in Computer Science, 130–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03265-3_14.

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Soulard, Raphaël, and Philippe Carré. "Colour Extension of Monogenic Wavelets with Geometric Algebra: Application to Color Image Denoising." In Quaternion and Clifford Fourier Transforms and Wavelets, 247–68. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0603-9_12.

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Shyjila, P. A., and M. Wilscy. "Non Local Means Image Denoising for Color Images Using PCA." In Advances in Computer Science and Information Technology, 288–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17857-3_29.

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Park, Hyun, and Young Shik Moon. "Automatic Denoising of 2D Color Face Images Using Recursive PCA Reconstruction." In Advanced Concepts for Intelligent Vision Systems, 799–809. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864349_73.

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Moreno, Rodrigo, Miguel Angel Garcia, Domenec Puig, and Carme Julià. "On Adapting the Tensor Voting Framework to Robust Color Image Denoising." In Computer Analysis of Images and Patterns, 492–500. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_60.

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Goossens, Bart, Hiêp Luong, Jan Aelterman, Aleksandra Pižurica, and Wilfried Philips. "A GPU-Accelerated Real-Time NLMeans Algorithm for Denoising Color Video Sequences." In Advanced Concepts for Intelligent Vision Systems, 46–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17691-3_5.

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Mújica-Vargas, Dante, Arturo Rendón-Castro, Manuel Matuz-Cruz, and Christian Garcia-Aquino. "Multi-core Median Redescending M-Estimator for Impulsive Denoising in Color Images." In Lecture Notes in Computer Science, 261–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4_25.

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Åström, Freddie, George Baravdish, and Michael Felsberg. "On Tensor-Based PDEs and Their Corresponding Variational Formulations with Application to Color Image Denoising." In Computer Vision – ECCV 2012, 215–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_16.

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Тези доповідей конференцій з теми "Color denoising"

Rabie, T. "Robust Color Video Denoising." In IEEE International Conference on Computer Systems and Applications, 2006. IEEE, 2006. http://dx.doi.org/10.1109/aiccsa.2006.205180.

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Huang, Xinjian, Bo Du, and Weiwei Liu. "Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/89.

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Анотація:

The R, G and B channels of a color image generally have different noise statistical properties or noise strengths. It is thus problematic to apply grayscale image denoising algorithms to color image denoising. In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose a MultiChannel Weighted Schatten p-Norm Minimization (MCWSNM) model for RGB color image denoising. More specifically, considering a small local RGB patch in a noisy image, we first find its nonlocal similar cubic patches in a search window with an appropriate size. These similar cubic patches are then vectorized and grouped to construct a noisy low-rank matrix, which can be recovered using the Schatten p-norm minimization framework. Moreover, a weight matrix is introduced to balance each channel’s contribution to the final denoising results. The proposed MCWSNM can be solved via the alternating direction method of multipliers. Convergence property of the proposed method are also theoretically analyzed . Experiments conducted on both synthetic and real noisy color image datasets demonstrate highly competitive denoising performance, outperforming comparison algorithms, including several methods based on neural networks.

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Saito, Takahiro, Nobuhiro Fujii, and Takashi Komatsu. "Iterative soft color-shrinkage for color-image denoising." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414246.

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Thomas, B. A., and J. J. Rodriguez. "Wavelet-based color image denoising." In Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899831.

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Dai, Jingjing, Oscar C. Au, Wen Yang, Chao Pang, Feng Zou, and Xing Wen. "Color video denoising based on adaptive color space conversion." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5538013.

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Meher, Sukadev. "Color Image Denoising with Multi-channel Spatial Color Filtering." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.60.

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Bettahar, S., P. Lambert, and A. Boudghene Stambouli. "Anisotropic color image denoising and sharpening." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025540.

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Joshi, N., C. L. Zitnick, R. Szeliski, and D. J. Kriegman. "Image deblurring and denoising using color priors." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206802.

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Teimouri, Mehdi, Ehsan Vahedi, Alireza Nasiri Avanaki, and Zabihollah Hasan Shahi. "An efficient denoising method for color images." In 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555310.

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Joshi, Neel, C. Lawrence Zitnick, Richard Szeliski, and David J. Kriegman. "Image deblurring and denoising using color priors." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206802.

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