The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Gonzalo R. Arce - One of the best experts on this subject based on the ideXlab platform.
-
Multi-spectral compressive snapshot imaging using RGB Image sensors
Optics Express, 2015Co-Authors: Hoover Rueda, Daniel Lau, Gonzalo R. ArceAbstract:Compressive sensing is a powerful sensing and reconstruction framework for recovering high dimensional signals with only a handful of observations and for spectral imaging, compressive sensing offers a novel method of multispectral imaging. Specifically, the coded aperture snapshot spectral Imager (CASSI) system has been demonstrated to produce multi-spectral data cubes color Images from a single snapshot taken by a Monochrome Image sensor. In this paper, we expand the theoretical framework of CASSI to include the spectral sensitivity of the Image sensor pixels to account for color and then investigate the impact on Image quality using either a traditional color Image sensor that spatially multiplexes red, green, and blue light filters or a novel Foveon Image sensor which stacks red, green, and blue pixels on top of one another.
-
RGB detectors on compressive snapshot multi-spectral Imagers
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015Co-Authors: Hoover Rueda, Gonzalo R. ArceAbstract:The coded aperture snapshot spectral Imager (CASSI) system has been demonstrated to produce spectral Images using just a single snapshot taken by a Monochrome Image sensor. In this paper, we introduce the use of color Image sensors in CASSI to include the spectral sensitivity of the Image sensor pixels to account for color and then investigate the impact on Image quality. We analyze the use of the traditional Bayer color filter array Image sensor, and the novel Foveon Image sensor which stacks red, green, and blue pixels on top of one another. Several simulations on different 3D spatio-spectral databases show improvements of up to 3 dBs in terms of PSNR over traditional Monochrome sensors.
-
digital color halftoning with generalized error diffusion and multichannel green noise masks
IEEE Transactions on Image Processing, 2000Co-Authors: Gonzalo R. Arce, Neal C GallagherAbstract:In this paper, we introduce two novel techniques for digital color halftoning with green-noise-stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike Monochrome Image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with Monochrome Image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.
Takahiro Saito - One of the best experts on this subject based on the ideXlab platform.
-
Image denoising with hard color-shrinkage and grouplet transform
28th Picture Coding Symposium, 2010Co-Authors: Takahiro Saito, Ken-ichi Ishikawa, Yasutaka Ueda, Takashi KomatsuAbstract:To remove signal-dependent noise of a digital color camera, we propose a new denoising method with our hard color-shrinkage in the tight-frame grouplet transform domain. The classic hard-shrinkage works well for Monochrome-Image denoising. To utilize inter-channel color dependence, a noisy Image undergoes the color transformation from the RGB to the luminance-and-chrominance color space, and the luminance and the chrominance components are separately denoised; but this approach cannot cope with actual signal-dependent noise. To utilize the noise's signal-dependencies, we have constructed the hard color-shrinkage where the interchannel color dependence is directly utilized in the RGB color space. The hard color-shrinkage alleviates denoising artifacts, and improves picture quality of denoised Images.
-
Universal sharpening-demosaicing for various types of color-filter array
2010 18th European Signal Processing Conference, 2010Co-Authors: Takashi Komatsu, Takahiro SaitoAbstract:A Monochrome-Image iterative deblurring method with the classic soft-shrinkage in the shift-invariant Haar wavelet transform domain was recently proposed by R. H. Chan et al. Extending this deblurring method, we present a new iterative sharpening-demosaicing method with the shift-invariant Haar wavelet transform and our color shrinkage utilizing redundant color transform. Our new sharpening-demosaicing method is originally constructed for the Bayer's primary color-filter array (CFA), but its minor modification renders it applicable to various CFA's other than the Bayer's CFA: the complementary CFA, the random arrangement CFA, and so on. Simulation results demonstrate that our new sharpening-demosaicing method in the shift-invariant Haar wavelet transform domain works much more efficiently than our previously proposed sharpening-demosaicing method with the totalvariation regularization in the spatial Image-domain.
-
Iterative soft color-shrinkage for color-Image denoising
2009 16th IEEE International Conference on Image Processing (ICIP), 2009Co-Authors: Takahiro Saito, Nobuhiro Fujii, Takashi KomatsuAbstract:To remove signal-dependent noise of a digital color camera, we present a new soft color-shrinkage scheme for color-Image denoising in a wavelet transform domain. The classic soft-shrinkage scheme works well for Monochrome-Image denoising; to utilize inter-channel color cross-correlations, a noisy Image undergoes the color-transformation from the RGB to the luminance-and-chrominance color space, and the luminance and the chrominance components are separately denoised. However, this color-denoising approach cannot cope with actual signal-dependent noise. To utilize the noise's signal-dependencies, we present an iterative soft color-shrinkage scheme where the inter-channel color cross-correlations are directly utilized in the RGB color space, and theoretically study its convergence property. Our color-shrinkage scheme alleviates denoising artifacts, and improves picture quality of denoised Images.
-
Sharpening-demosaicing with the shift-invariant Haar wavelet transform
2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2009Co-Authors: Takashi Komatsu, Takahiro SaitoAbstract:A Monochrome-Image iterative deblurring method with the classical soft-shrinkage scheme in the shift-invariant Haar wavelet transform domain was recently proposed by R. H. Chan et al (2003). Extending the method, we present a new iterative sharpening-demosaicing method with the shift-invariant Haar wavelet transform and our previously proposed soft color-shrinkage utilizing inter-channel color cross-correlations. Our new iterative method performs demosaicing and sharpening simultaneously in the wavelet transform domain. Simulation results demonstrate that our new method works as efficiently as our previously proposed sharpening-demosaicing method with the TV regularization in the spatial Image-domain.
Jin Korekuni - One of the best experts on this subject based on the ideXlab platform.
-
A colorization algorithm based on local MAP estimation
Pattern Recognition, 2006Co-Authors: Hideki Noda, Jin Korekuni, Michiharu NiimiAbstract:This paper presents a colorization algorithm which adds color to Monochrome Images. In this paper, the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color Image given a Monochrome Image. Markov random field (MRF) is used for modeling a color Image which is utilized as a prior for the MAP estimation. The MAP estimation problem for a whole Image is decomposed into local MAP estimation problems for each pixel. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color Images with 25.7-32.6dB PSNR values for eight Images.
-
Simple and Efficient Colorization in YCbCr Color Space
18th International Conference on Pattern Recognition (ICPR'06), 2006Co-Authors: Hideki Noda, Michiharu Niimi, Jin KorekuniAbstract:We have already proposed a colorization method in RGB color space, where the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color Image given a Monochrome Image. Markov random field (MRF) is used for modeling a color Image which is utilized as a prior for the MAP estimation. In this paper, a colorization method in YCbCr space is presented, which is derived by the same formulation as in RGB color space. The presented method in YCbCr space is much simpler than that in RGB space and requires much less computation time: about one fourth of computation time in RGB space. As for quality of estimated color Image, both methods in YCbCr and RGB space produce color Images with comparable PSNR values
Takashi Komatsu - One of the best experts on this subject based on the ideXlab platform.
-
Image denoising with hard color-shrinkage and grouplet transform
28th Picture Coding Symposium, 2010Co-Authors: Takahiro Saito, Ken-ichi Ishikawa, Yasutaka Ueda, Takashi KomatsuAbstract:To remove signal-dependent noise of a digital color camera, we propose a new denoising method with our hard color-shrinkage in the tight-frame grouplet transform domain. The classic hard-shrinkage works well for Monochrome-Image denoising. To utilize inter-channel color dependence, a noisy Image undergoes the color transformation from the RGB to the luminance-and-chrominance color space, and the luminance and the chrominance components are separately denoised; but this approach cannot cope with actual signal-dependent noise. To utilize the noise's signal-dependencies, we have constructed the hard color-shrinkage where the interchannel color dependence is directly utilized in the RGB color space. The hard color-shrinkage alleviates denoising artifacts, and improves picture quality of denoised Images.
-
Universal sharpening-demosaicing for various types of color-filter array
2010 18th European Signal Processing Conference, 2010Co-Authors: Takashi Komatsu, Takahiro SaitoAbstract:A Monochrome-Image iterative deblurring method with the classic soft-shrinkage in the shift-invariant Haar wavelet transform domain was recently proposed by R. H. Chan et al. Extending this deblurring method, we present a new iterative sharpening-demosaicing method with the shift-invariant Haar wavelet transform and our color shrinkage utilizing redundant color transform. Our new sharpening-demosaicing method is originally constructed for the Bayer's primary color-filter array (CFA), but its minor modification renders it applicable to various CFA's other than the Bayer's CFA: the complementary CFA, the random arrangement CFA, and so on. Simulation results demonstrate that our new sharpening-demosaicing method in the shift-invariant Haar wavelet transform domain works much more efficiently than our previously proposed sharpening-demosaicing method with the totalvariation regularization in the spatial Image-domain.
-
Iterative soft color-shrinkage for color-Image denoising
2009 16th IEEE International Conference on Image Processing (ICIP), 2009Co-Authors: Takahiro Saito, Nobuhiro Fujii, Takashi KomatsuAbstract:To remove signal-dependent noise of a digital color camera, we present a new soft color-shrinkage scheme for color-Image denoising in a wavelet transform domain. The classic soft-shrinkage scheme works well for Monochrome-Image denoising; to utilize inter-channel color cross-correlations, a noisy Image undergoes the color-transformation from the RGB to the luminance-and-chrominance color space, and the luminance and the chrominance components are separately denoised. However, this color-denoising approach cannot cope with actual signal-dependent noise. To utilize the noise's signal-dependencies, we present an iterative soft color-shrinkage scheme where the inter-channel color cross-correlations are directly utilized in the RGB color space, and theoretically study its convergence property. Our color-shrinkage scheme alleviates denoising artifacts, and improves picture quality of denoised Images.
-
Sharpening-demosaicing with the shift-invariant Haar wavelet transform
2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2009Co-Authors: Takashi Komatsu, Takahiro SaitoAbstract:A Monochrome-Image iterative deblurring method with the classical soft-shrinkage scheme in the shift-invariant Haar wavelet transform domain was recently proposed by R. H. Chan et al (2003). Extending the method, we present a new iterative sharpening-demosaicing method with the shift-invariant Haar wavelet transform and our previously proposed soft color-shrinkage utilizing inter-channel color cross-correlations. Our new iterative method performs demosaicing and sharpening simultaneously in the wavelet transform domain. Simulation results demonstrate that our new method works as efficiently as our previously proposed sharpening-demosaicing method with the TV regularization in the spatial Image-domain.
Hideki Noda - One of the best experts on this subject based on the ideXlab platform.
-
Bayesian colorization using MRF color Image modeling
Lecture Notes in Computer Science, 2020Co-Authors: Hideki Noda, Nobuteru Takao, Hitoshi Korekuni, Michiharu NiimiAbstract:This paper presents a colorization algorithm which produces color Images from given Monochrome Images. Unlike previously proposed colorization methods, this paper formulates the colorization problem as the maximum a posteriori (MAP) estimation of a color Image given a Monochrome Image. Markov random field (MRF) is used for modeling a color Image which is utilized as a priori information for the MAP estimation. Under the mean field approximation, The MAP estimation problem for a whole Image can be decomposed into local MAP estimation problems for each pixel. The local MAP estimation is described as a simple quadratic programming problem with constraints. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color Images with 25.7 dB to 32.6 dB PSNR values for four standard Images.
-
Colorization in YCbCr Space and its Application to Improve Quality of JPEG Color Images
2007 IEEE International Conference on Image Processing, 2007Co-Authors: Hideki Noda, Nobuteru Takao, Michiharu NiimiAbstract:This paper presents a colorization method in YCbCr color space, which is based on the maximum a posteriori estimation of a color Image given a Monochrome Image as is our previous method in RGB color space. The presented method in YCbCr space is much simpler than that in RGB space and requires much less computation time, while both methods in YCbCr and RGB space produce color Images with comparable PSNR values. The proposed colorization in YCbCr is applied to JPEG compressed color Images aiming at better recovery of down sampled chrominance planes. Experimental results show that colorization in YCbCr is usually effective for quality improvement of JPEG color Images.
-
A colorization algorithm based on local MAP estimation
Pattern Recognition, 2006Co-Authors: Hideki Noda, Jin Korekuni, Michiharu NiimiAbstract:This paper presents a colorization algorithm which adds color to Monochrome Images. In this paper, the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color Image given a Monochrome Image. Markov random field (MRF) is used for modeling a color Image which is utilized as a prior for the MAP estimation. The MAP estimation problem for a whole Image is decomposed into local MAP estimation problems for each pixel. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color Images with 25.7-32.6dB PSNR values for eight Images.
-
Simple and Efficient Colorization in YCbCr Color Space
18th International Conference on Pattern Recognition (ICPR'06), 2006Co-Authors: Hideki Noda, Michiharu Niimi, Jin KorekuniAbstract:We have already proposed a colorization method in RGB color space, where the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color Image given a Monochrome Image. Markov random field (MRF) is used for modeling a color Image which is utilized as a prior for the MAP estimation. In this paper, a colorization method in YCbCr space is presented, which is derived by the same formulation as in RGB color space. The presented method in YCbCr space is much simpler than that in RGB space and requires much less computation time: about one fourth of computation time in RGB space. As for quality of estimated color Image, both methods in YCbCr and RGB space produce color Images with comparable PSNR values
-
PCM (2) - Bayesian colorization using MRF color Image modeling
Advances in Multimedia Information Processing - PCM 2005, 2005Co-Authors: Hideki Noda, Nobuteru Takao, Hitoshi Korekuni, Michiharu NiimiAbstract:This paper presents a colorization algorithm which produces color Images from given Monochrome Images. Unlike previously proposed colorization methods, this paper formulates the colorization problem as the maximum a posteriori (MAP) estimation of a color Image given a Monochrome Image. Markov random field (MRF) is used for modeling a color Image which is utilized as a priori information for the MAP estimation. Under the mean field approximation, The MAP estimation problem for a whole Image can be decomposed into local MAP estimation problems for each pixel. The local MAP estimation is described as a simple quadratic programming problem with constraints. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color Images with 25.7 dB to 32.6 dB PSNR values for four standard Images.
