1,209 research outputs found
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing
Single-image haze removal is a long-standing hurdle for computer vision
applications. Several works have been focused on transferring advances from
image classification, detection, and segmentation to the niche of image
dehazing, primarily focusing on contrastive learning and knowledge
distillation. However, these approaches prove computationally expensive,
raising concern regarding their applicability to on-the-edge use-cases. This
work introduces a simple, lightweight, and efficient framework for single-image
haze removal, exploiting rich "dark-knowledge" information from a lightweight
pre-trained super-resolution model via the notion of heterogeneous knowledge
distillation. We designed a feature affinity module to maximize the flow of
rich feature semantics from the super-resolution teacher to the student
dehazing network. In order to evaluate the efficacy of our proposed framework,
its performance as a plug-and-play setup to a baseline model is examined. Our
experiments are carried out on the RESIDE-Standard dataset to demonstrate the
robustness of our framework to the synthetic and real-world domains. The
extensive qualitative and quantitative results provided establish the
effectiveness of the framework, achieving gains of upto 15\% (PSNR) while
reducing the model size by 20 times.Comment: Preprint version. Accepted at Opti
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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