The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring
Open accessAbstract
We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · arXiv · doi:10.48550/arxiv.2005.12872 · Cited by 18,253
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
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“To alleviate this problem, most approaches use the down-sampling strategy to reduce the spatial resolution of features [23].”
MethodSwinIR: Image Restoration Using Swin Transformer
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Restormer: Efficient Transformer for High-Resolution Image Restoration
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“…model the global contexts and achieves significant progress in lots of high-level vision tasks (e.g., image classification [11], object detection [1,31] and semantic segmentation [25,32]), it has been developed to solve image super-resolution [10], image deblurring [21,27] and image denoise [2,24].”
BackgroundPre-Trained Image Processing Transformer
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Metadata
- Authors
- Kong, L., Dong, J., Ge, J., Li, M., & Pan, J.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 5886-5895
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075642
- Cited by
- 398
- References
- 33
- FWCI
- 45.14
- Type
- conference paper
- Language
- en
- Open access
- green
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- Kong, L., Dong, J., Ge, J., Li, M., & Pan, J. (2023). Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.00570
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