Edge-integrated image denosing using a dual branch convolutional neural network

In image denoising, preserving the object edges and fine texture details is crucial for restoring high-quality images. However, most image denoising methods fail to integrate edge information, resulting in excessive smoothing of the denoised images. In this paper, we propose a lightweight edge-integrated image denoising convolutional neural network (EIID), which comprises two parallel branches, namely image branch and the edge branch, to separately handle the input noisy image and its object edge layer. First, EIID obtains the object edge layer of an input noisy image using Laplacian operator. Second, the noisy image is input into the image branch to extract global and local features, while the object edge layer is input into the edge branch, to extract edge features. Finally, a mask fusion strategy is adopted to dynamically fuse the global and local features of the input image and its edge detail features. The fused features are then processed through convolutional operations to recovery the denoised image. Experimental results demonstrate the outstanding performance of EIID on synthetic and real noise datasets. EIID achieves a PSNR/SSIM improvement of 0.13[Formula: see text]dB/0.003 on the grayscale dataset Urban100 and 0.22[Formula: see text]dB/0.001 on the color dataset BSD68 with noise intensity of 25. Moreover, on the real noise dataset PolyU, it achieves a PSNR/SSIM improvement of 0.17[Formula: see text]dB/0.001. Additionally, the parameter count of EIID is one-sixteenth that of Restomer, and its number of floating-point operations is one-fourth.