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GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions

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Abstract

Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves as a backbone for image restoration under adverse weather conditions. GridFormer is designed in a grid structure using a residual dense transformer block, and it introduces two core designs. First, it uses an enhanced attention mechanism in the transformer layer. The mechanism includes stages of the sampler and compact self-attention to improve efficiency, and a local enhancement stage to strengthen local information. Second, we introduce a residual dense transformer block (RDTB) as the final GridFormer layer. This design further improves the network’s ability to learn effective features from both preceding and current local features. The GridFormer framework achieves state-of-the-art results on five diverse image restoration tasks in adverse weather conditions, including image deraining, dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The source code and pre-trained models will be released.

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Fig. 1: Comparison results for image restoration in adverse weather conditions
Fig. 2: GridFormer architecture
Fig. 3: Grid unit structure and information flow
Fig. 4: The structure of the proposed Residual Dense Transformer Block (RDTB)
Fig. 5: Right
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Data Availability Statement

The datasets generated during and/or analyzed during the current study are available in the WeatherDiffusion repository, with the link as https://github.com/IGITUGraz/WeatherDiffusion.

References

  • Ali, A. M., Benjdira, B., Koubaa, A., El-Shafai, W., Khan, Z., & Boulila, W. (2023). Vision transformers in image restoration: A survey. Sensors, 23(5), 2385.

    Article  Google Scholar 

  • Ba, Y., Zhang, H., Yang, E., Suzuki, A., Pfahnl, A., Chandrappa, C.C., de Melo, C.M., You, S., Soatto, S. & Wong, A. et al. (2022). Not just streaks: Towards ground truth for single image deraining. In: Proceedings of European Conference on Computer Vision, pp. 723–740.

  • Berman, D. & Avidan, S. et al. (2016). Non-local image dehazing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 1674–1682.

  • Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016). Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11), 5187–5198.

    Article  MathSciNet  Google Scholar 

  • Cai, Y., Zhou, Y., Han, Q., Sun, J., Kong, X., Li, J. & Zhang, X. (2022). Reversible column networks. In: Proceedings of international conference on learning representations

  • Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A. & Zagoruyko, S. (2020). End-to-end object detection with transformers. In: Proceedings of European Conference on Computer Vision, pp. 213–229.

  • Charbonnier, P., Blanc-Feraud, L., Aubert, G. & Barlaud, M. (1994). Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of international conference on image processing, pp. 168–172.

  • Chen, D., He, M., Fan, Q., Liao, J., Zhang, L., Hou, D., Yuan, L. & Hua, G. (2019). Gated context aggregation network for image dehazing and deraining. In: Proceedings of IEEE winter conference on applications of computer vision, pp. 1375–1383.

  • Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C. & Gao, W. (2021). Pre-trained image processing transformer. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 12299–12310.

  • Chen, S., Ye, T., Liu, Y., Chen, E., Shi, J. & Zhou, J. (2022). Snowformer: Scale-aware transformer via context interaction for single image desnowing. arXiv preprint arXiv:2208.09703.

  • Chen, W. T., Fang, H. Y., Ding, J. J., Tsai, C. C. & Kuo, S. Y. (2020). Jstasr: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In: Proceedings of European conference on computer vision, pp. 754–770.

  • Chen, Y. L. & Hsu, C. T. (2013). A generalized low-rank appearance model for spatio-temporally correlated rain streaks. In: Proceedings of IEEE international conference on computer vision, pp. 1968–1975.

  • Cho, S. J., Ji, S. W., Hong, J. P., Jung, S. W. & Ko, S. J. (2021). Rethinking coarse-to-fine approach in single image deblurring. In: Proceedings of the IEEE international conference on computer vision, pp. 4641–4650.

  • Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F. & Yang, M. H. (2020). Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 2157–2167.

  • Dong, J. & Pan, J. (2020). Physics-based feature dehazing networks. In: Proceedings of European conference on computer vision, pp. 188–204.

  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G. & Gelly, S. et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of international conference on learning representations.

  • Du, Y., Xu, J., Zhen, X., Cheng, M. M., & Shao, L. (2020). Conditional variational image deraining. IEEE Transactions on Image Processing, 29, 6288–6301.

    Article  Google Scholar 

  • Fu, X., Huang, J., Ding, X., Liao, Y., & Paisley, J. (2017). Clearing the skies: A deep network architecture for single-image rain removal. IEEE Transactions on Image Processing, 26(6), 2944–2956.

    Article  MathSciNet  Google Scholar 

  • Garg, K. & Nayar, S. K. (2005). When does a camera see rain? In: Proceedings of IEEE international conference on computer vision, pp. 1067–1074.

  • Glorot, X., Bordes, A. & Bengio, Y. (2011). Deep sparse rectifier neural networks. In: Proceedings of international conference on artificial intelligence and statistics, pp. 315–323.

  • Gu, X., Wang, L., Deng, Z., Cao, Y., Huang, X. & Zhu, Y. m. (2023). Adafuse: Adaptive medical image fusion based on spatial-frequential cross attention. arXiv preprint arXiv:2310.05462.

  • He, K., Sun, J., & Tang, X. (2010). Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341–2353.

    Google Scholar 

  • Hsu, W. Y. & Chang, W. C. (2023). Wavelet approximation-aware residual network for single image deraining. IEEE transactions on pattern analysis and machine intelligence.

  • Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. (2017). Densely connected convolutional networks. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 4700–4708.

  • Isola, P., Zhu, J.Y., Zhou, T. & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 1125–1134.

  • Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.

    Article  Google Scholar 

  • Jaw, D. W., Huang, S. C., & Kuo, S. Y. (2020). Desnowgan: An efficient single image snow removal framework using cross-resolution lateral connection and Gans. IEEE Transactions on Circuits and Systems for Video Technology, 31(4), 1342–1350.

    Article  Google Scholar 

  • Jiang, K., Wang, Z., Yi, P., Chen, C., Han, Z., Lu, T., Huang, B., & Jiang, J. (2020). Decomposition makes better rain removal: An improved attention-guided Deraining network. IEEE Transactions on Circuits and Systems for Video Technology, 31(10), 3981–3995.

    Article  Google Scholar 

  • Jiang, K., Wang, Z., Yi, P., Chen, C., Huang, B., Luo, Y., Ma, J. & Jiang, J. (2020). Multi-scale progressive fusion network for single image deraining. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 8346–8355.

  • Jiang, K., Wang, Z., Yi, P., Chen, C., Wang, Z., Wang, X., Jiang, J., & Lin, C. W. (2021). Rain-free and residue hand-in-hand: A progressive coupled network for real-time image deraining. IEEE Transactions on Image Processing, 30, 7404–7418.

    Article  Google Scholar 

  • Johnson, J., Alahi, A. & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of European conference on computer vision, pp. 694–711.

  • Kang, L. W., Lin, C. W., & Fu, Y. H. (2011). Automatic single-image-based rain streaks removal via image decomposition. IEEE Transactions on Image Processing, 21(4), 1742–1755.

    Article  MathSciNet  Google Scholar 

  • Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C. & Lo, W. Y. et al. (2023). Segment anything. arXiv preprint arXiv:2304.02643.

  • Lee, H., Choi, H., Sohn, K. & Min, D. (2022). Knn local attention for image restoration. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 2139–2149.

  • Li, B., Liu, X., Hu, P., Wu, Z., Lv, J. & Peng, X. (2022). All-in-one image restoration for unknown corruption. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 17452–17462.

  • Li, B., Peng, X., Wang, Z., Xu, J. & Feng, D. (2017). Aod-net: All-in-one dehazing network. In: Proceedings of IEEE international conference on computer vision, pp. 4770–4778.

  • Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., & Wang, Z. (2018). Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1), 492–505.

    Article  MathSciNet  Google Scholar 

  • Li, J., Li, D., Xiong, C. & Hoi, S. (2022). Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: Proceedings of International Conference on Machine Learning, pp. 12888–12900.

  • Li, P., Yun, M., Tian, J., Tang, Y., Wang, G., & Wu, C. (2019). Stacked dense networks for single-image snow removal. Neurocomputing, 367, 152–163.

    Article  Google Scholar 

  • Li, R., Cheong, L. F. & Tan, R. T. (2019). Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 1633–1642.

  • Li, R., Tan, R. T. & Cheong, L. F. (2020). All in one bad weather removal using architectural search. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 3175–3185.

  • Li, X., Hua, Z., & Li, J. (2022). Two-stage single image dehazing network using Swin-transformer. IET Image Processing, 16(9), 2518–2534.

    Article  Google Scholar 

  • Li, X., Wu, J., Lin, Z., Liu, H. & Zha, H. (2018). Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: Proceedings of European conference on computer vision, pp. 254–269.

  • Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L. & Timofte, R. (2021). Swinir: Image restoration using swin transformer. In: Proceedings of IEEE international conference on computer vision, pp. 1833–1844.

  • Liang, Y., Anwar, S. & Liu, Y. (2022). Drt: A lightweight single image deraining recursive transformer. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 589–598.

  • Liu, X., Ma, Y., Shi, Z. & Chen, J. (2019). Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of IEEE international conference on computer vision, pp. 7314–7323.

  • Liu, X., Suganuma, M., Sun, Z. & Okatani, T. (2019). Dual residual networks leveraging the potential of paired operations for image restoration. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 7007–7016.

  • Liu, Y., Zhu, L., Pei, S., Fu, H., Qin, J., Zhang, Q., Wan, L. & Feng, W. (2021). From synthetic to real: Image dehazing collaborating with unlabeled real data. In: Proceedings of ACM international conference on multimedia, pp. 50–58.

  • Liu, Y. F., Jaw, D. W., Huang, S. C., & Hwang, J. N. (2018). Desnownet: Context-aware deep network for snow removal. IEEE Transactions on Image Processing, 27(6), 3064–3073.

    Article  MathSciNet  Google Scholar 

  • Loshchilov, I. & Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983.

  • Loshchilov, I. & Hutter, F. (2019). Decoupled weight decay regularization. In: Proceedings of international conference on learning representations.

  • Luo, Y., Xu, Y. & Ji, H. (2015). Removing rain from a single image via discriminative sparse coding. In: Proceedings of IEEE international conference on computer vision, pp. 3397–3405.

  • Narasimhan, S. G. & Nayar, S. K. (2000). Chromatic framework for vision in bad weather. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 598–605.

  • Özdenizci, O. & Legenstein, R. (2023). Restoring vision in adverse weather conditions with patch-based denoising diffusion models. IEEE transactions on pattern analysis and machine intelligence.

  • Petit, O., Thome, N., Rambour, C., Themyr, L., Collins, T. & Soler, L. (2021). U-net transformer: Self and cross attention for medical image segmentation. In: Proceedings of machine learning in medical imaging, pp. 267–276.

  • Qian, R., Tan, R. T., Yang, W., Su, J. & Liu, J. (2018). Attentive generative adversarial network for raindrop removal from a single image. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 2482–2491.

  • Qiao, Y., Huo, Z. & Meng, S. (2023). Dual-route synthetic-to-real adaption for single image dehazing. IET Image Processing.

  • Qin, X., Wang, Z., Bai, Y., Xie, X. & Jia, H. (2020). Ffa-net: Feature fusion attention network for single image dehazing. In: Proceedings of AAAI conference on artificial intelligence, pp. 11908–11915.

  • Qu, Y., Chen, Y., Huang, J. & Xie, Y. (2019). Enhanced pix2pix dehazing network. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 8160–8168.

  • Quan, R., Yu, X., Liang, Y. & Yang, Y. (2021). Removing raindrops and rain streaks in one go. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 9147–9156.

  • Quan, Y., Deng, S., Chen, Y. & Ji, H. (2019). Deep learning for seeing through window with raindrops. In: Proceedings of IEEE international conference on computer vision, pp. 2463–2471.

  • Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X. & Yang, M. H. (2016). Single image dehazing via multi-scale convolutional neural networks. In: Proceedings of European conference on computer vision, pp. 154–169.

  • Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W. & Yang, M. H. (2018). Gated fusion network for single image dehazing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 3253–3261.

  • Ren, W., Tian, J., Han, Z., Chan, A. & Tang, Y. (2017). Video desnowing and deraining based on matrix decomposition. In: Proceedings of IEEE Conference on computer vision and pattern recognition, pp. 4210–4219.

  • Roth, S., & Black, M. J. (2005). Fields of experts: A framework for learning image priors. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2, 860–867.

    Google Scholar 

  • Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D. & Wang, Z. (2016). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 1874–1883.

  • Simonyan, K. & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

  • Song, Y., He, Z., Qian, H. & Du, X. (2022). Vision transformers for single image dehazing. arXiv preprint arXiv:2204.03883.

  • Susladkar, O., Deshmukh, G., Makwana, D., Mittal, S., Teja, R. & Singhal, R. (2023). Gafnet: A global fourier self attention based novel network for multi-modal downstream tasks. In: Proceedings of the IEEE winter conference on applications of computer vision, pp. 5242–5251.

  • Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A. & Li, Y. (2022). Maxim: Multi-axis mlp for image processing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 5769–5780.

  • Valanarasu, J. M. J., Yasarla, R. & Patel, V. M. (2022). Transweather: Transformer-based restoration of images degraded by adverse weather conditions. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 2353–2363.

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. In: Proceedings of advances in neural information processing systems (2017).

  • Wang, H., Xie, Q., Zhao, Q. & Meng, D. (2020). A model-driven deep neural network for single image rain removal. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 3103–3112.

  • Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2020). Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3349–3364.

    Article  Google Scholar 

  • Wang, T., Yang, X., Xu, K., Chen, S., Zhang, Q. & Lau, R. W. (2019). Spatial attentive single-image deraining with a high quality real rain dataset. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 12270–12279.

  • Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B. & Lu, T. (2023). Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method. In: Proceedings of AAAI conference on artificial intelligence.

  • Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y. & Change Loy, C. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision workshops.

  • Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J. & Li, H. (2022). Uformer: A general u-shaped transformer for image restoration. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 17683–17693.

  • Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., Xie, Y. & Ma, L. (2021). Contrastive learning for compact single image dehazing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 10551–10560.

  • Xiao, J., Fu, X., Liu, A., Wu, F. & Zha, Z. J. (2022). Image de-raining transformer. IEEE transactions on pattern analysis and machine intelligence.

  • Yamashita, A., Tanaka, Y. & Kaneko, T. (2005). Removal of adherent waterdrops from images acquired with stereo camera. In: Proceedings of international conference on intelligent robots and systems, pp. 400–405.

  • Yang, W., Tan, R. T., Feng, J., Guo, Z., Yan, S., & Liu, J. (2019). Joint rain detection and removal from a single image with contextualized deep networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), 1377–1393.

    Article  Google Scholar 

  • Yang, W., Tan, R. T., Feng, J., Liu, J., Guo, Z. & Yan, S. (2017). Deep joint rain detection and removal from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1357–1366.

  • Yao, C., Jin, S., Liu, M., & Ban, X. (2022). Dense residual transformer for image denoising. Electronics, 11(3), 418.

    Article  Google Scholar 

  • Ye, T., Jiang, M., Zhang, Y., Chen, L., Chen, E., Chen, P. & Lu, Z. (2021). Perceiving and modeling density is all you need for image dehazing. In: Proceedings of European conference on computer vision.

  • Ye, T., Zhang, Y., Jiang, M., Chen, L., Liu, Y., Chen, S. & Chen, E. (2022). Perceiving and modeling density for image dehazing. In: Proceedings of European conference on computer vision, pp. 130–145.

  • Yin, X., Tu, G. & Chen, Q. (2023). Multiscale depth fusion with contextual hybrid enhancement network for image dehazing. IEEE transactions on instrumentation and measurement.

  • You, S., Tan, R. T., Kawakami, R., Mukaigawa, Y., & Ikeuchi, K. (2015). Adherent raindrop modeling, detection and removal in video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1721–1733.

    Article  Google Scholar 

  • Yu, H., Zheng, N., Zhou, M., Huang, J., Xiao, Z. & Zhao, F. (2022). Frequency and spatial dual guidance for image dehazing. In: Proceedings of European conference on computer vision, pp. 181–198.

  • Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X. & Wang, J. (2021). Hrformer: High-resolution vision transformer for dense predict. In: Proceedings of advances in neural information processing systems, pp. 7281–7293.

  • Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S. & Yang, M. H. (2022). Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 5728–5739.

  • Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M. H. & Shao, L. (2021). Multi-stage progressive image restoration. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 14821–14831.

  • Zhang, F., Chen, G., Wang, H. & Zhang, C. (2024). Cf-dan: Facial-expression recognition based on cross-fusion dual-attention network. Computational Visual Media pp. 1–16.

  • Zhang, H. & Patel, V. M. (2018). Densely connected pyramid dehazing network. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 3194–3203.

  • Zhang, H. & Patel, V. M. (2018). Density-aware single image de-raining using a multi-stream dense network. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 695–704.

  • Zhang, H., Sindagi, V., & Patel, V. M. (2019). Image de-raining using a conditional generative adversarial network. IEEE Transactions on Circuits and Systems for Video Technology, 30(11), 3943–3956.

  • Zhang, K., Li, R., Yu, Y., Luo, W., & Li, C. (2021). Deep dense multi-scale network for snow removal using semantic and depth priors. IEEE Transactions on Image Processing, 30, 7419–7431.

    Article  Google Scholar 

  • Zhang, T., Jiang, N., Lin, J., Lin, J. & Zhao, T. (2022). Desnowformer: an effective transformer-based image desnowing network. In: Proceedings of IEEE international conference on visual communications and image processing, pp. 1–5.

  • Zhang, X., Wang, T., Wang, J., Tang, G., & Zhao, L. (2020). Pyramid channel-based feature attention network for image dehazing. Computer Vision and Image Understanding, 197, 103003.

    Article  Google Scholar 

  • Zhang, Y., Tian, Y., Kong, Y., Zhong, B. & Fu, Y. (2018). Residual dense network for image super-resolution. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 2472–2481.

  • Zhang, Z., Zhu, Y., Fu, X., Xiong, Z., Zha, Z. J. & Wu, F. (2021). Multifocal attention-based cross-scale network for image de-raining. In: Proceedings of the 29th ACM international conference on multimedia, pp. 3673–3681.

  • Zheng, L., Li, Y., Zhang, K. & Luo, W. (2022). T-net: Deep stacked scale-iteration network for image dehazing. IEEE Transactions on Multimedia.

  • Zhu, J. Y., Park, T., Isola, P. & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of IEEE international conference on computer vision, pp. 2223–2232.

  • Zhu, L., Fu, C. W., Lischinski, D. & Heng, P. A. (2017). Joint bi-layer optimization for single-image rain streak removal. In: Proceedings of IEEE international conference on computer vision, pp. 2526–2534.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62372223, 62372480), in part by the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012839), in part by Shenzhen Science and Technology Program (No. JSGG20220831093004008), in part by China Mobile Zijin Innovation Insititute (No. NR2310J7M).

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Authors and Affiliations

  1. Nanjing University, Nanjing, 210023, China

    Tao Wang, Ziqian Shao & Tong Lu

  2. Harbin Institute of Technology, Shenzhen, 518055, China

    Kaihao Zhang

  3. Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Wenhan Luo

  4. Rakuten Institute of Technology, Tokyo, Japan

    Bjorn Stenger

  5. KAIST, Daejeon, South Korea

    Tae-Kyun Kim

  6. Imperial College London, London, UK

    Tae-Kyun Kim

  7. Tencent, Shenzhen, 518107, China

    Wei Liu

  8. Austranlian National University, Canberra, Australia

    Hongdong Li

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Wang, T., Zhang, K., Shao, Z. et al. GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02056-0

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