Recovering a clean background: a new progressive multi-scale CNN for image denoising

Convolutional neural networks (CNNs) have gained significant popularity in image denoising. In recent years, many CNN-based image denoising methods have been developed. However, some of these methods extract the noise from noisy images by only stacking numerous relatively common convolutional layers, which can easily lead to some issues, including more loss of image details and insufficient processing of complex real-world noisy images. To address these challenges more effectively, a new progressive multi-scale denoising network (PMSDNet) is proposed. Its denoising effectiveness is attributed to two key modules, namely progressive multi-scale fusion block (PMSFB) and pixel attention block (PAB), in which PMSFB enhances the noise extraction capability of PMSDNet by enlarging its receptive field and reducing important image feature information loss, and PAB further facilitates PMSDNet to effectively capture the noise and preserve more image details by guiding it to focus more on multi-noised pixels and high-frequency image regions. Experimental results demonstrate the inspiring denoising performance of PMSDNet in facing three types of image noises, i.e., non-blind synthetic noise, blind synthetic noise and real-world noise. Additionally, the PMSDNet is extended to single-image deraining, showing the promising deraining performance.