Journal ArticleDOI
Color demosaicking via directional linear minimum mean square-error estimation
Lei Zhang,Xiaolin Wu +1 more
TLDR
The experimental results show that the presented color demosaicking technique outperforms the existing methods both in PSNR measure and visual perception.Abstract:
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.read more
Citations
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Proceedings ArticleDOI
Non-local sparse models for image restoration
TL;DR: Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.
Journal ArticleDOI
Loss Functions for Image Restoration With Neural Networks
TL;DR: It is shown that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged, and a novel, differentiable error function is proposed.
Journal ArticleDOI
Deep joint demosaicking and denoising
TL;DR: A new data-driven approach forDemosaicking and denoising is introduced: a deep neural network is trained on a large corpus of images instead of using hand-tuned filters and this network and training procedure outperform state-of-the-art both on noisy and noise-free data.
Journal ArticleDOI
Color demosaicking by local directional interpolation and nonlocal adaptive thresholding
TL;DR: Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images.
Proceedings ArticleDOI
Image demosaicing: a systematic survey
TL;DR: In this paper, a survey of over seventy published works in this field since 1999 is provided, with a collection of eleven competing algorithms whose source codes or executables are provided by the authors.
References
More filters
Patent
Color imaging array
TL;DR: In this article, a mosaic of selectively transmissive filters is superposed in registration with a solid state imaging array having a broad range of light sensitivity, the distribution of filter types in the mosaic being in accordance with the above-described patterns.
Journal ArticleDOI
Cubic splines for image interpolation and digital filtering
Hsieh Hou,H. Andrews +1 more
TL;DR: Applications to image and signal processing include interpolation, smoothing, filtering, enlargement, and reduction, and experimental results are presented for illustrative purposes in two-dimensional image format.
Patent
Adaptive color plane interpolation in single sensor color electronic camera
James E. Adams,John F. Hamilton +1 more
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Journal ArticleDOI
Color plane interpolation using alternating projections
TL;DR: This paper presents a new demosaicing technique that uses inter-channel correlation effectively in an alternating-projections scheme and outperforms various state-of-the-art demosaice techniques, both visually and in terms of mean square error.
Journal ArticleDOI
Demosaicing: image reconstruction from color CCD samples
TL;DR: The answer is to let the edges support the color information, and the color channels support the edges, and thereby achieve better perceptual results than those that are bounded by the sampling theoretical limit.