Learning Real-World Image De-Weathering with Imperfect Supervision

• URL: http://arxiv.org/abs/2310.14958v3
• Date: Mon, 25 Dec 2023 02:17:04 GMT
• Title: Learning Real-World Image De-Weathering with Imperfect Supervision
• Authors: Xiaohui Liu and Zhilu Zhang and Xiaohe Wu and Chaoyu Feng and Xiaotao Wang and Lei Lei and Wangmeng Zuo
• Abstract summary: Existing real-world de-weathering datasets often exhibit inconsistent illumination, position, and textures between the ground-truth images and the input degraded images. We develop a Consistent Label Constructor (CLC) to generate a pseudo-label as consistent as possible with the input degraded image. We combine the original imperfect labels and pseudo-labels to jointly supervise the de-weathering model by the proposed Information Allocation Strategy.
• Score: 57.748585821252824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world image de-weathering aims at removing various undesirable weather-related artifacts. Owing to the impossibility of capturing image pairs concurrently, existing real-world de-weathering datasets often exhibit inconsistent illumination, position, and textures between the ground-truth images and the input degraded images, resulting in imperfect supervision. Such non-ideal supervision negatively affects the training process of learning-based de-weathering methods. In this work, we attempt to address the problem with a unified solution for various inconsistencies. Specifically, inspired by information bottleneck theory, we first develop a Consistent Label Constructor (CLC) to generate a pseudo-label as consistent as possible with the input degraded image while removing most weather-related degradations. In particular, multiple adjacent frames of the current input are also fed into CLC to enhance the pseudo-label. Then we combine the original imperfect labels and pseudo-labels to jointly supervise the de-weathering model by the proposed Information Allocation Strategy (IAS). During testing, only the de-weathering model is used for inference. Experiments on two real-world de-weathering datasets show that our method helps existing de-weathering models achieve better performance. Codes are available at https://github.com/1180300419/imperfect-deweathering.

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