Single Image Layer Separation via Deep Admm Unrolling

Abstract

Single image layer separation aims to divide the observed image into two independent components according to special task requirements and has been widely used in many vision and multimedia applications. Because this task is fundamentally ill-posed, most existing approaches tend to design complex priors on the separated layers. However, the cost function with complex prior regularization is hard to optimize. The performance is also compromised by fixed iteration schemes and less data fitting ability. More importantly, it is also challenging to design a unified framework to separate image layers for different applications. To partially mitigate the above limitations, we develop a flexible optimization unrolling technique to incorporate deep architectures into iterations for adaptive image layer separation. Specifically, we first design a general energy model with implicit priors and adopt the widely used alternating direction method of multiplier (ADMM) to establish our basic iteration scheme. By unrolling with residual convolution architectures, we successfully obtain a simple, flexible, and data-dependent image separation method. Extensive experiments on the tasks of rain streak removal and reflection removal validate the effectiveness of our approach.