The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Benchmarking Single-Image Dehazing and Beyond
Abstract
In this paper, we present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of stateof- the-art dehazing algorithms, and suggest promising future directions.
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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ImageNet Large Scale Visual Recognition Challenge
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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Cited by
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3051462 · Cited by 2,715
Self-citationDEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention
Chen, Z., He, Z., & Lu, Z.-M. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3354108 · Cited by 938
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
MAXIM: Multi-Axis MLP for Image Processing
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00568 · Cited by 616
Low-Light Image Enhancement via a Deep Hybrid Network
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910412 · Cited by 508
Self-citation
Metadata
- Authors
- Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., & Wang, Z.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2019-01
- Volume
- 28
- Issue
- 1
- Pages
- 492-505
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2963928582
- Cited by
- 2,301
- References
- 12
- FWCI
- 61.44
- Type
- journal article
- Language
- en
- Open access
- closed
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