Blind Convolution Denoising of Real Image Based on Attention and Feature Fusion
This paper proposes a denoising algorithm based on attention and feature fusion based on CBDNet.Specifi-cally,in the upsampling process,more relevant features are extracted by adding the attention mechanism of space and channel fusion,and the input of jump connection is redefined for feature fusion.Use maximum pooling instead of average pooling during downsampling to enhance image texture and detail.In terms of experiment,this paper tests on three real noise data sets,such as SIDD,NC12 and Nam,and compared them with several advanced algorithms.The experimental results show that the algorithm is superior in quantitative and visual aspects.
real image denoisingattention mechanismskip connectionpooling
万方数据
维普
