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Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Wang, X., Xie, L., Dong, C., and Shan, Y · 1914
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Bayesian-based iterative method of image restoration
Richardson, W. H · 1972
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., and Malik, J · 2001
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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On single image scale-up using sparse-representations
Zeyde, R., Elad, M., and Protter, M · 2010
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Learning photographic global tonal adjustment with a database of input/output image pairs
Bychkovsky, V., Paris, S., Chan, E., and Durand, F · 2011
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Image smoothing via l 0 gradient minimization
Xu, L., Lu, C., Xu, Y., and Jia, J · 2011
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M. L · 2012
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Fast local laplacian filters: Theory and applications
Aubry, M., Paris, S., Hasinoff, S. W., Kautz, J., and Durand, F · 2014
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Image super-resolution using deep convolutional networks
Dong, C., Loy, C. C., He, K., and Tang, X · 2015
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Single image super-resolution from transformed self-exemplars
Huang, J.-B., Singh, A., and Ahuja, N · 2015
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Accelerating the super-resolution convolutional neural network
Dong, C., Loy, C. C., and Tang, X · 2016
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E. and Timofte, R · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Sketch-based manga retrieval using manga109 dataset
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., and Aizawa, K · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L · 2017
Cited alongside, same era.
Deblurgan: Blind motion deblurring using conditional adversarial networks
Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., and Matas, J · 2018
Cited alongside, same era.
Benchmarking single-image dehazing and beyond
Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., and Wang, Z · 2018
Cited alongside, same era.
Deep retinex decomposition for low-light enhancement
Wei, C., Wang, W., Yang, W., and Liu, J · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Language models are few-shot learners
All-in-one image restoration for unknown corruption
Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., and Peng, X · 2022
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Very lightweight photo retouching network with conditional sequential modulation
Liu, Y., He, J., Chen, X., Zhang, Z., Zhao, H., Dong, C., and Qiao, Y · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2022
Later among the works it cites.
Restormer: Efficient transformer for high-resolution image restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., and Yang, M.-H · 2022
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Understanding and improving visual prompting: A label-mapping perspective
Chen, A., Yao, Y., Chen, P.-Y., Zhang, Y., and Liu, S · 2023
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Cited alongside, same era.
Pre-trained image processing transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., and Gao, W · 2021
Cited alongside, same era.
Swinir: Image restoration using swin transformer
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R · 2021
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2021
Cited alongside, same era.
Multi-stage progressive image restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., and Shao, L · 2021
Cited alongside, same era.
Designing a practical degradation model for deep blind image super-resolution
Zhang, K., Liang, J., Van Gool, L., and Timofte, R · 2021
Cited alongside, same era.
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
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2023
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Prores: Exploring degradation-aware visual prompt for universal image restoration
Ma, J., Cheng, T., Wang, G., Zhang, Q., Wang, X., and Zhang, L · 2023
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Promptir: Prompting for all-in-one blind image restoration
Potlapalli, V., Zamir, S. W., Khan, S., and Khan, F. S · 2023
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Exploring effective factors for improving visual in-context learning
Sun, Y., Chen, Q., Wang, J., Wang, J., and Li, Z · 2023
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Larger language models do in-context learning differently
Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., Chen, X., Liu, H., Huang, D., Zhou, D., et al · 2023
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Track anything: Segment anything meets videos
Yang, J., Gao, M., Li, Z., Gao, S., Wang, F., and Zheng, F · 2023
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Inpaint anything: Segment anything meets image inpainting
Yu, T., Feng, R., Feng, R., Liu, J., Jin, X., Zeng, W., and Chen, Z · 2023
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What makes good examples for visual in-context learning?
Zhang, Y., Zhou, K., and Liu, Z · 2023
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