Iterative Contrastive Learning for Single Image Raindrop Removal

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Iterative Contrastive Learning for Single Image Raindrop Removal
Title:
Iterative Contrastive Learning for Single Image Raindrop Removal
Journal Title:
2022 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
18 October 2022
Citation:
Xulei, Y., Peisheng, Q., Li, W., Shenghao, Z., Cen, C., Xiaoli, L., & Zeng, Z. (2022). Iterative Contrastive Learning for Single Image Raindrop Removal. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9897979
Abstract:
Deep learning has achieved remarkable progress in computer vision and image analysis. However, raindrop removal from single image still remains challenging, due to a wide range of raindrop diversities and surface reflections. In this paper, we propose an iterative neural network with feedback strategy and contrastive learning for single image raindrop removal. First, we design an iterative feedback neural network to refine low-level representations with high-level information, i.e., the output of the previous iteration is used as input for the next iteration, together with the input image with raindrops. As a result, raindrops could be gradually removed through this feedback manner. Then, we deploy contrastive regularization to push the restored image from each iteration close to the clean images without raindrops, but away from rainy images with raindrops. Extensive experiments on two raindrop benchmark datasets demonstrate the effectiveness of the proposed approach in comparison with the state-of-the-art methods. The methodology in this work could be further extended to self-supervised contrastive learning to obtain robust feature representations with less labelled data.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG2-RP-2021-027
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
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