Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images

TL;DR

The paper introduces DMGN, a unified deep learning framework that effectively restores clean backgrounds from superimposed images with various noise types, outperforming specialized methods across multiple tasks.

Contribution

It proposes the Deep-Masking Generative Network with a novel Residual Deep-Masking Cell for progressive background restoration across diverse noise conditions.

Findings

Outperforms state-of-the-art methods in reflection removal, deraining, and dehazing.

Employs a coarse-to-fine generative process for high-quality background recovery.

Utilizes noise as a contrasting cue to enhance background refinement.

Abstract

Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to the diverse and complicated appearance patterns of noise layers within the image. In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise. Our proposed DMGN follows a coarse-to-fine generative process: a coarse background image and a noise image are first generated in parallel, then the noise image is further leveraged to refine the background image to achieve a higher-quality background image. In particular, we design the novel Residual Deep-Masking Cell as the core operating unit for our DMGN to enhance the effective information and suppress the negative information during image generation via learning a gating mask to control the information flow. By iteratively employing this Residual Deep-Masking Cell, our proposed DMGN is able to generate both high-quality background image and noisy image progressively. Furthermore, we propose a two-pronged strategy to effectively leverage the generated noise image as contrasting cues to facilitate the refinement of the background image. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain steak removal and image dehazing, show that our DMGN consistently outperforms state-of-the-art methods specifically designed for each single task.