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Existing image restoration approaches typically employ extensive networks specifically trained for designated degradations.
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Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshop (CVPRW). pp. 1132–1140 (2017)
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Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: Proc. Int. Conf. on Learning Representations (ICLR)(2017)
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Zhang, K., Zuo, W., Zhang, L.: Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Trans. Image Processing (TIP)pp. 4608–4622 (2018)
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Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proc. European Conf. on Computer Vision (ECCV). pp. 294–310 (2018)
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. Int. Conf. on Learning Representations (ICLR)(2021)
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Fan, Q., Chen, D., Yuan, L., Hua, G., Yu, N., Chen, B.: A general decoupled learning framework for parameterized image operators. IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI)pp. 33–47 (2021)
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Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. In: Proc. Annual Meeting of the Association for Computational Linguistics and Int. Joint Conf. on Natural Language Processing (ACL/IJCNLP). pp. 4582–4597 (2021)
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Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 2472–2481 (2018)
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Bapna, A., Firat, O.: Simple, scalable adaptation for neural machine translation. In: Proc. Conf. on Empirical Methods in Natural Language Processing and Int. Joint Conf. on Language Processing (EMNLP-IJCNLP). pp. 1538–1548 (2019)
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Dai, T., Cai, J., Zhang, Y., Xia, S., Zhang, L.: Second-order attention network for single image super-resolution. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 11065–11074 (2019)
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Gao, H., Tao, X., Shen, X., Jia, J.: Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 3848–3856 (2019)
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Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for NLP. In: Proc. Int. Conf. on Machine Learning (ICML). pp. 2790–2799 (2019)
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Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In: Proc. IEEE Int. Conf. on Computer Vision (ICCV). pp. 8877–8886 (2019)
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Liu, X., Ma, Y., Shi, Z., Chen, J.: Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proc. IEEE Int. Conf. on Computer Vision (ICCV). pp. 7313–7322 (2019)
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2021
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Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: Proc. IEEE Int. Conf. on Computer Vision Workshop (ICCVW). pp. 1833–1844 (2021)
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Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Multi-stage progressive image restoration. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 14821–14831 (2021)
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Chen, S., Ge, C., Tong, Z., Wang, J., Song, Y., Wang, J., Luo, P.: Adaptformer: Adapting vision transformers for scalable visual recognition. In: Proc. Conf. on Neural Information Processing Systems (NeurIPS)(2022)
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Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-In-One Image Restoration for Unknown Corruption. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 17431–17441 (2022)
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Li, D., Zhang, Y., Cheung, K.C., Wang, X., Qin, H., Li, H.: Learning degradation representations for image deblurring. In: Proc. European Conf. on Computer Vision (ECCV). pp. 736–753 (2022)
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Valanarasu, J.M.J., Yasarla, R., Patel, V.M.: Transweather: Transformer-based restoration of images degraded by adverse weather conditions. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 2343–2353 (2022)
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Potlapalli, V., Zamir, S.W., Khan, S., Khan, F.S.: Promptir: Prompting for all-in-one blind image restoration. In: Proc. Conf. on Neural Information Processing Systems (NeurIPS)(2023)
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Yang, Z., Huang, J., Chang, J., Zhou, M., Yu, H., Zhang, J., Zhao, F.: Visual recognition-driven image restoration for multiple degradation with intrinsic semantics recovery. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 14059–14070 (2023)
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