81 research outputs found
Joint Depth Estimation and Mixture of Rain Removal From a Single Image
Rainy weather significantly deteriorates the visibility of scene objects,
particularly when images are captured through outdoor camera lenses or
windshields. Through careful observation of numerous rainy photos, we have
found that the images are generally affected by various rainwater artifacts
such as raindrops, rain streaks, and rainy haze, which impact the image quality
from both near and far distances, resulting in a complex and intertwined
process of image degradation. However, current deraining techniques are limited
in their ability to address only one or two types of rainwater, which poses a
challenge in removing the mixture of rain (MOR). In this study, we propose an
effective image deraining paradigm for Mixture of rain REmoval, called
DEMore-Net, which takes full account of the MOR effect. Going beyond the
existing deraining wisdom, DEMore-Net is a joint learning paradigm that
integrates depth estimation and MOR removal tasks to achieve superior rain
removal. The depth information can offer additional meaningful guidance
information based on distance, thus better helping DEMore-Net remove different
types of rainwater. Moreover, this study explores normalization approaches in
image deraining tasks and introduces a new Hybrid Normalization Block (HNB) to
enhance the deraining performance of DEMore-Net. Extensive experiments
conducted on synthetic datasets and real-world MOR photos fully validate the
superiority of the proposed DEMore-Net. Code is available at
https://github.com/yz-wang/DEMore-Net.Comment: 11 pages, 7 figures, 5 table
Blind Image Decomposition
We propose 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 toward
real-world vision systems. To facilitate research on this new task, we
construct multiple 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.Comment: ECCV 2022. Project page:
https://junlinhan.github.io/projects/BID.html. Code:
https://github.com/JunlinHan/BI
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