Image denoising based on a CNN model

Abstract

In digital image processing, filtering noise to reconstruct a high quality image is an important work for further image processing such as object segmentation, detection, recognition and tracking, etc. In this paper, we will use a CNN model in deep learning for image denoising. Compared with traditional image denoising methods such as average filtering, Wiener filtering and median filtering, the advantage of using this CNN model is that the parameters of this model can be optimized through network training; whereas in traditional image denoising, the parameters of these algorithms are fixed and cannot be adjusted during the filtering, namely, lack of adaptivity. In this paper, we design and implement the denoising method based on a linear CNN model. Our experimental results show that the proposed CNN model can effectively remove Gaussian noise and improve the performance of traditional image filtering methods significantly.