One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Yuda Song, Zhuqing He, Hui Qian, Xin Du
Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, convolutional neural network-based methods have dominated image dehazing. However, vision Transformers, which has recently made a breakthrough in high-level vision tasks, has not brought new dimensions to image dehazing. We start with the popular Swin Transformer and find that several of its key designs are unsuitable for image dehazing. To this end, we propose DehazeFormer, which consists of various improvements, such as the modified normalization layer, activation function, and spatial information aggregation scheme. We train multiple variants of DehazeFormer on various datasets to demonstrate its effectiveness. Specifically, on the most frequently used SOTS indoor set, our small model outperforms FFA-Net with only 25% #Param and 5% computational cost. To the best of our knowledge, our large model is the first method with the PSNR over 40 dB on the SOTS indoor set, dramatically outperforming the previous state-of-the-art methods. We also collect a large-scale realistic remote sensing dehazing dataset for evaluating the method's capability to remove highly non-homogeneous haze. We share our code and dataset at https://github.com/IDKiro/DehazeFormer.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Song, Y., He, Z., Qian, H., & Du, X. (2023). Vision Transformers for Single Image Dehazing. IEEE Transactions on Image Processing, 32, 1927–1941. https://doi.org/10.1109/tip.2023.3256763
Song, Yuda, et al. "Vision Transformers for Single Image Dehazing." IEEE Transactions on Image Processing, vol. 32, 2023, pp. 1927–1941, https://doi.org/10.1109/tip.2023.3256763.
Song, Yuda, Zhuqing He, Hui Qian, and Xin Du. "Vision Transformers for Single Image Dehazing." IEEE Transactions on Image Processing 32 (2023): 1927–1941. https://doi.org/10.1109/tip.2023.3256763.
Song Y., He Z., Qian H. [и др.] Vision Transformers for Single Image Dehazing // IEEE Transactions on Image Processing. — 2023. — Vol. 32. — P. 1927–1941. — URL: https://doi.org/10.1109/tip.2023.3256763
@article{song2023vision,
title = {{Vision Transformers for Single Image Dehazing}},
author = {Yuda Song and Zhuqing He and Hui Qian and Xin Du},
journal = {IEEE Transactions on Image Processing},
year = {2023},
volume = {32},
pages = {1927--1941},
doi = {10.1109/tip.2023.3256763},
url = {https://doi.org/10.1109/tip.2023.3256763}
}TY - JOUR AU - Song, Yuda AU - He, Zhuqing AU - Qian, Hui AU - Du, Xin TI - Vision Transformers for Single Image Dehazing JO - IEEE Transactions on Image Processing VL - 32 SP - 1927 EP - 1941 PY - 2023 DA - 2023/01/01 DO - 10.1109/tip.2023.3256763 UR - https://doi.org/10.1109/tip.2023.3256763 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.