Pixel-Attention CNN With Color Correlation Loss for Color Image Denoising

Fan Jia, Liyan Ma, Yijin Yang, Tieyong Zeng

Abstract: Convolutional neural networks (CNNs) have been applied to many image processing tasks and achieve great successes. In order to extract common features, every pixel in an image shares the same filters. However, pixels in different regions of an image varies dramatically and shared filters may lose some important local information. Rather than shared filters, smart filters which can be adapted to image context should be designed to better remove noise which occurs randomly in noisy image. Meanwhile, current CNN architectures compute the loss of each color channel independently, regardless of the potential color information. In this letter, we proposed a pixel-attention convolutional neural network (PACNN) with color correlation loss for the color image denoising task. The pixel-attention mechanism could generate pixel-wise attention maps which help remove random noise. The color correlation loss exploits color correlation to further improve denoising performance on color noisy images. The experimental results on several standard datasets demonstrate the state-of-the-art (SOTA) performance and the superiority of the proposed method.