Deep convolutional architecture for natural image denoising

Natural image is an important source of human access to information, however observed image signals are often corrupted in the process of acquisition or transmission. As an important link of image preprocessing, image denoising has significant influence on the follow-up procedures. Unlike traditional methods that use related features of spatial or transform domain in a single image, we propose a deep learning method for natural image denoising. Our method directly learns an end-to-end mapping from a noisy image to a corresponding de-noised image. It's based on a deep convolutional architecture with rectified linear units and local response normalization. The experiment results show that the proposed deep convolutional architecture learns various features from noisy images, and achieves denoising results of high quality within short time for practical usage.