Give me a hint: an explicit prior based image denoising

The performance of an image denoising method highly depends on its ability to produce smoothed homogeneous regions and preserve fine-grained texture details in the restored image. Existing denoising methods rely on implicit learning of Convolutional Neural Networks (CNNs) to restore an image. We show that the implicitly learned representations are limited in capacity, resulting in a sub-optimal pixel estimation of homogeneous regions and loss in texture details. In this work, we argue that explicitly introducing low and high frequency information enhances the representational capacity of denoising CNNs. To this end, we propose XPrior that provides the network with two explicit priors, i.e., a Gaussian smoothed image and an Edge map. This simple yet effective integration of priors with the noisy image enhances the model’s ability to restore images with high perceived quality. The proposed approach can easily be plugged into the existing denoising methods. Comprehensive experimental results show the superiority of the proposed approach that achieves a remarkable performance gain of 1.23 dB and 1.49 dB average PSNR on BSD68 dataset over DRUNet (Zhang in IEEE Trans Pattern Anal Mach Intell 10.1109/TPAMI.2021.3088914, 2021) and DnCNN (Zhang in IEEE Trans Image Process 26:3142–3155, 2017), respectively.