The Experts below are selected from a list of 25080 Experts worldwide ranked by ideXlab platform
Philip Ogunbona - One of the best experts on this subject based on the ideXlab platform.
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method of color interpolation in a single sensor color camera using Green Channel separation
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Chaminda Weerasinghe, I Kharitonenko, Philip OgunbonaAbstract:This paper presents a color interpolation algorithm for a single sensor color camera. The proposed algorithm is especially designed to solve the problem of pixel crosstalk among the pixels of different color Channels. InterChannel cross-talk gives rise to blocking effects on the interpolated Green plane, and also spreading of false colors into detailed structures. The proposed algorithm separates the Green Channel into two planes, one highly correlated with the red Channel and the other with the blue Channel. These separate planes are used for red and blue Channel interpolation. Experiments conducted on McBeth color chart and natural images have shown that the proposed algorithm can eliminate or suppress blocking and color artifacts to produce better quality images.
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
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joint denoising and demosaicking with Green Channel prior for real world burst images
IEEE Transactions on Image Processing, 2021Co-Authors: Shi Guo, Zhetong Liang, Lei ZhangAbstract:Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has been achieved to perform denoising and demosaicking jointly. However, most existing CNN-based joint denoising and demosaicking (JDD) methods work on a single image while assuming additive white Gaussian noise, which limits their performance on real-world applications. In this work, we study the JDD problem for real-world burst images, namely JDD-B. Considering the fact that the Green Channel has twice the sampling rate and better quality than the red and blue Channels in CFA raw data, we propose to use this Green Channel prior (GCP) to build a GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from Green Channels are utilized to guide the feature extraction and feature upsampling of the whole image. To compensate for the shift between frames, the offset is also estimated from GCP features to reduce the impact of noise. Our GCP-Net can preserve more image structures and details than other JDD methods while removing noise. Experiments on synthetic and real-world noisy images demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.
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color reproduction from noisy cfa data of single sensor digital cameras
IEEE Transactions on Image Processing, 2007Co-Authors: Lei Zhang, David ZhangAbstract:Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are Channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color Channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color Channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution Green Channel is recovered. The second step involves in a wavelet-based denoising process to remove the CFA Channel-dependent noises from the reconstructed Green Channel. The red and blue Channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.
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color demosaicking via directional linear minimum mean square error estimation
IEEE Transactions on Image Processing, 2005Co-Authors: Lei Zhang, Xiaolin WuAbstract:Digital cameras sample scenes using a color filter array of mosaic pattern (e.g., the Bayer pattern). The demosaicking of the color samples is critical to the image quality. This paper presents a new color demosaicking technique of optimal directional filtering of the Green-red and Green-blue difference signals. Under the assumption that the primary difference signals (PDS) between the Green and red/blue Channels are low pass, the missing Green samples are adaptively estimated in both horizontal and vertical directions by the linear minimum mean square-error estimation (LMMSE) technique. These directional estimates are then optimally fused to further improve the Green estimates. Finally, guided by the demosaicked full-resolution Green Channel, the other two color Channels are reconstructed from the LMMSE filtered and fused PDS. The experimental results show that the presented color demosaicking technique outperforms the existing methods both in PSNR measure and visual perception.
Gwanggil Jeon - One of the best experts on this subject based on the ideXlab platform.
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reconstruction of missing color Channel data using a three step back propagation neural network
International Journal of Machine Learning and Cybernetics, 2019Co-Authors: Jin Wang, Marco Anisetti, Gwanggil JeonAbstract:Demosaicking aims to approximate missing color pixels through analysis of the geometric structure between given color pixels and missing color pixels. In this paper, we introduce an efficient adaptive demosaicking method based on back propagation (BP) neural network (BP-NN). We firstly reconstruct the Green Channel using one BPNN, and then refine the Green Channel utilizing another BPNN based on the color difference. With the whole Green Channel interpolated, we reconstruct the red/blue Channel using the color difference between the Green Channel and red/blue Channel in a local region. Finally, we refine the red/blue Channel using the third BPNN. Regarding the interpolation issue, different image features have completely different properties, such as smooth regions, edges, and textures. Consequently, it is necessary to identify an adaptive model to estimate the relation among neighboring color pixels. We provide the adaptive BP-NN based demosaicking algorithm which can reduce blurring through recovery of missing pixels by a learning process, and also use a pre-trained fixed network to reduce computational complexity. Experimental results demonstrate that the proposed method outperforms extant approaches in PSNR, computational complexity, and visual quality.
Chaminda Weerasinghe - One of the best experts on this subject based on the ideXlab platform.
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method of color interpolation in a single sensor color camera using Green Channel separation
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Chaminda Weerasinghe, I Kharitonenko, Philip OgunbonaAbstract:This paper presents a color interpolation algorithm for a single sensor color camera. The proposed algorithm is especially designed to solve the problem of pixel crosstalk among the pixels of different color Channels. InterChannel cross-talk gives rise to blocking effects on the interpolated Green plane, and also spreading of false colors into detailed structures. The proposed algorithm separates the Green Channel into two planes, one highly correlated with the red Channel and the other with the blue Channel. These separate planes are used for red and blue Channel interpolation. Experiments conducted on McBeth color chart and natural images have shown that the proposed algorithm can eliminate or suppress blocking and color artifacts to produce better quality images.
Asoke K Nandi - One of the best experts on this subject based on the ideXlab platform.
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novel and adaptive contribution of the red Channel in pre processing of colour fundus images
Journal of The Franklin Institute-engineering and Applied Mathematics, 2007Co-Authors: Nancy M Salem, Asoke K NandiAbstract:Abstract A new pre-processing method for colour fundus images with adaptive contribution of the red Channel is proposed. Based on a condition that is developed in this paper, this method utilises the intensity information from both red and Green Channels instead of using only the Green Channel as in the usual practice. The histogram matching is used to modify the histogram of the Green Channel by using the histogram of the red Channel (of the same retinal image) to obtain a new processed image having the advantages of both Channels. This method can be used to correct non-uniform illumination in colour fundus images or as a pre-processing step in the automatic analysis of retinal images. Results show that the use of histogram matched (HM) image give better performance than using the Green Channel image when employing the two-dimensional matched filter to detect retinal blood vessels. At specificity of 90%, in case of abnormal images, sensitivity increased from 76% when using the Green Channel image to 82% when using the HM image compared with 81% when using the piece-wise threshold probing method. In case of normal images, at the same specificity, the sensitivity obtained when using Green Channel image or HM image was 87% compared with 88% for the piece-wise threshold probing method.
