EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

In this paper, we propose an edge-focused image denoising convolutional neural network for the restoration of noisy images corrupted with additive white Gaussian noise (AWGN). First, the edge information for an input image is obtained for each RGB channel using the simple Laplacian edge operator on a smoothened image using the smoothen mask. Second, residual convolutional blocks, where each block comprises two parallel depth layers (one for an image channel and the other for respective edge channels), perform image and edge processing. Finally, the processed image and edge features are mixed and mapped using a single convolutional layer into the restored image. Our proposed parallel image and edge processing blocks can recover edges and fine structures while smoothing out the noise. In addition, our proposed network is efficiently designed such that the total number of weight parameters can be considerably reduced compared with conventional methods. Experimental results show that the proposed model is more effective in preserving textures and edges while removing noise and achieves up to 0.8 dB higher PSNR and 0.05 higher SSIM on a real image dataset than conventional methods.