Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Additive spread-spectrum watermark detection in demosaicked images
Proceedings of the 11th ACM workshop on Multimedia and security
PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras
IEEE Transactions on Image Processing
Regularization approaches to demosaicking
IEEE Transactions on Image Processing
Self-similarity driven color demosaicking
IEEE Transactions on Image Processing
Joint demosaicking and denoising with space-varying filters
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Edge adaptive color demosaicking based on the spatial correlation of the bayer color difference
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Color image demosaicking: An overview
Image Communication
Novel color demosaicking for noisy color filter array data
Signal Processing
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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.