A Lightweight Channel Correlation Invertible Network for Image Denoising

Abstract

In recent years, deep learning has made significant progress in image denoising. However, the complexity of advanced methods' systems is also increasing, which will increase the calculation cost and hinder the convenient analysis and comparison of methods. Therefore, a lightweight model based on invertible networks is proposed. The invertible network has great advantages in image denoising. It is lightweight, memory-saving, and information-lossless in backpropagation. To effectively remove the noise and restore a clean image, the high-frequency part of the image is resampled and modeled to remove the impact of noise better. The channel context block is proposed to better focus on useful channels and improve the network's perception of useful information in images while ensuring the complexity and computing cost. At the same time, the residual structure with channel correlation modeling is used to extract the features in the convolutional flow, to effectively retain the details and texture of the image, and learn more details of the spatial features of the image, so as to prevent the blur and distortion of the image in the denoising process. The proposed method allows the model to enjoy lower computational complexity on the premise of ensuring performance.