Investigating Collaborative Layer Projection for Robust Rain Scene Modeling

Abstract

Rain is a common weather phenomenon that severely degrades outdoor image quality, affecting information extraction. The existing methods can be roughly classified as model-based and data-driven approaches. Model-based methods tend to utilize complex but limiting priors, resulting in optimization difficulty. Data-driven techniques emphasize the establishment of a network architecture and strongly depend on training pairs; hence, they become invalid in practical scenarios. To mitigate these problems, we develop a flexible collaborative layer projection framework for efficient and effective single-image rain streak removal task. We introduce a solution space to standardize fidelity without loss of generality to build a general deraining model. Then, a Collaborative Layer Projection Framework (CLPF) is presented for solving this model. Using the projection framework, various types of techniques (e.g., learnable architectures and optimization models) can be easily integrated to realize the desired performance. Extensive evaluations of synthetic and real rain images demonstrate that the proposed method outperforms state-of-the-art methods. In addition, we extend the method to other visual areas, such as video deraining and image dehazing. Our framework also performs relatively well on these issues.

Keywords

• Deep learning
• image deraining
• image dehazing
• image restoration
• video deraining