Blind Image Decomposition

Abstract

We present and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step towards real-world vision systems. To facilitate research on this new task, we construct three benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN. Code and project page are available.

Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal

“…In this line of research, Hu et al [7] built an imaging model for rain streaks and haze based on the visual effect of rain and the scene depth map to synthesize a realistic dataset named RainCityscapes. Han et al [9] constructed a superimposed image dataset and proposed a simple yet general Blind Image Decomposition Network to decompose rain streaks, raindrops, and haze in a blind image decomposition setting. Kim et al [8] proposed a frequency-based model for removing rain and haze, where the frequency-based model divided the input image into high-frequency and low-frequency parts with a guided filter and then employed a symmetric encoder-decoder network to remove rain and haze separately.…”

“…All experiments are evaluated on NVIDIA GTX 2080ti GPUs. In the experiments for RainCityscapes [7] and BID Rain datasets [9], the input images are resized to 512 × 256. For the SPA-Data, we follow the practice in [24], that uses original images with size of 256 × 256.…”

“…Many efforts have been made to solve the individual rain removal and haze removal tasks [4]- [6]. However, only a few works consider removing the rain and haze jointly in scene images [7]- [9]. In the realworld scenario, it is a very common situation that the rain and haze co-occur in the rainfall environment (see Fig.…”

“…However, methods designed for certain contamination cannot handle the complex real-world scenario with the co-occurrence of the rain and haze in the natural scenes. Recent studies also pointed out the necessity of joint-removal, such as Han et al [9] decompose rain and haze by a Blind Image Decomposition Network, and Kim et al [8] remove rain and haze by a frequencybased model. A new dataset for the purpose of benchmarking joint rain and haze removal, named RainCityScapes, is also proposed to facilitate research on this important task [7].…”

“…1f). The proposed ADU-Net can effectively remove the different contamination in the images and compensates for the scene information on multiple benchmark datasets, including RainCityscapes [7], BID rain [9] and SPA-Data [24].…”