Image Denoising Algorithm Based on Multi-Scale Fusion and Adaptive Attention Mechanism
Zhitong Su, Siyu Han, Keqing Ning, Wei Guo Song, Guanyuan Li · 2023
An adaptive attention mechanism algorithm based on multiscale fusion is proposed to solve the nonuniform blind blur of image denoising algorithm for depth of field, camera wobble and object motion. Firstly, the convolutional neural network residual module (MRESNET) was established to extract the deep features of the image, and the image pyramid was used as input to the model. Channel attentional mechanism (SE) and spatial attentional mechanism fusion (CBAM) were inserted into the network to improve local and spatial image representation. This paper introduces Generative Antagonism Network (GAN), and because of the sparseness of noise image, sets up discriminator, separates fuzzy information from useful information in training iteration, and then distinguishes real image from de-noise image. Experimental results show that the proposed algorithm improves peak SNR (PSNR) and image similarity (SSIM) and can restore higher quality images.