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 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.

中文翻译：

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深度掩蔽生成网络：叠加图像背景恢复的统一框架


从包含噪声层的叠加图像中恢复干净的背景是图像恢复经典任务类别（例如图像反射去除、图像去雨和图像去雾）的共同关键。由于图像中噪声层的外观模式多样且复杂，这些任务通常是单独制定和处理的。在这项工作中，我们提出了深度掩蔽生成网络（DMGN），它是从叠加图像中恢复背景的统一框架，能够应对不同类型的噪声。我们提出的DMGN遵循从粗到细的生成过程：首先并行生成粗略背景图像和噪声图像，然后进一步利用噪声图像来细化背景图像以获得更高质量的背景图像。特别是，我们设计了新颖的Residual Deep-Masking Cell作为DMGN的核心操作单元，通过学习门控掩模来控制信息流，从而在图像生成过程中增强有效信息并抑制负面信息。通过迭代地使用这种残差深度掩蔽单元，我们提出的 DMGN 能够逐步生成高质量的背景图像和噪声图像。此外，我们提出了一种双管齐下的策略，有效地利用生成的噪声图像作为对比线索，以促进背景图像的细化。对图像背景恢复的三个典型任务（包括图像反射去除、图像雨排去除和图像去雾）进行的广泛实验表明，我们的 DMGN 始终优于专门为每个任务设计的最先进方法。