Deep Signal-Dependent Denoising Noise Algorithm

Although many existing noise parameter estimations of image signal-dependent noise have certain denoising effects, most methods are not ideal. There are some problems with these methods, such as poor noise suppression effects, smooth details, lack of flexible denoising ability, etc. To solve these problems, in this study, we propose a deep signal-dependent denoising noise algorithm. The algorithm combines the model method with a convolutional neural network. We use the noise level of the noise image and the noise image together as the input of the convolutional neural network to obtain a wider range of noise levels than the single noise image as the input. In the convolutional neural network, the deep features of the image are extracted by multi-layer residuals, which solves the difficult problem of training. Extensive experiments demonstrate that our noise parameter estimation has good denoising performance.