DOF: A Demand-Oriented Framework for Image Denoising

Most existing image denoising methods focus on improving denoising quality. However, when applying denoising methods to practical tasks, in addition to the denoising quality, the number of parameters, and the computational complexity should be fully considered. In this article, we propose a demand-oriented framework (DOF) for image denoising, which can give preference to the number of parameters, the computational complexity, and the denoising quality or balance these three performance metrics. To perform the demand-oriented denoising, we first design a scale encoder to help the denoising model extract fewer but more representative features. Then, the split-flow module is introduced to fully exploit the input features by sharing the information of one network branch with other network branches. Finally, the scale decoder is utilized to reconstruct the final noise map without using any parameters. Through extensive experiments, we demonstrate that the proposed framework can be applied to several existing methods to help them achieve a more competitive denoising performance in terms of the number of parameters, and computational complexity.