Efficient deep learning of image denoising using patch complexity local divide and deep conquer

Image denoising is a fundamental task in computer vision and image processing domain. In recent years, the task has been tackled with deep neural networks by learning the patterns of noises and image patches. However, because of the high diversity of natural image patches and noise distributions, a huge network with a large amount of training data is necessary to obtain a state-of-the-art performance. In this paper, we propose a novel ensemble...