Residual Learning for Effective joint Demosaicing-Denoising
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Image demosaicing and denoising are key steps for color image production pipeline. The classical processing sequence consists in applying denoising first, and then demosaicing. However, this sequence leads to oversmoothing and unpleasant checkerboard effect. Moreover, it is very difficult to change this order, because once the image is demosaiced, the statistical properties of the noise will be changed dramatically. This is extremely challenging for the traditional denoising models that strongly rely on statistical assumptions. In this paper, we attempt to tackle this prickly problem. Indeed, here we invert the traditional CFA processing pipeline by first applying demosaicing and then using an adapted denoising. In order to obtain high-quality demosaicing of noiseless images, we combine the advantages of traditional algorithms with deep learning. This is achieved by training convolutional neural networks (CNNs) to learn the residuals of traditional algorithms. To improve the performance in image demosaicing we propose a modified Inception architecture. Given the trained demosaicing as a basic component, we apply it to noisy images and use another CNN to learn the residual noise (including artifacts) of the demosaiced images, which allows to reconstruct full color images. Experimental results show clearly that this method outperforms several state-of-the-art methods both quantitatively as well as in terms of visual quality.
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