Cross-Scale KNN Image Transformer for Image Restoration
- 주제(키워드) deblurring , denoising , deraining , Image restoration , k-nn search , low-level vision , self-attention , transformer , transformer
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000204162
- 본문언어 영어
- Published As https://doi.org/10.1109/ACCESS.2023.3242556
초록/요약
Numerous image restoration approaches have been proposed based on attention mechanism, achieving superior performance to convolutional neural networks (CNNs) based counterparts. However, they do not leverage the attention model in a form fully suited to the image restoration tasks. In this paper, we propose an image restoration network with a novel attention mechanism, called cross-scale k -NN image Transformer (CS-KiT), that effectively considers several factors such as locality, non-locality, and cross-scale aggregation, which are essential to image restoration. To achieve locality and non-locality, the CS-KiT builds k -nearest neighbor relation of local patches and aggregates similar patches through local attention. To induce cross-scale aggregation, we ensure that each local patch embraces different scale information with scale-aware patch embedding (SPE) which predicts an input patch scale through a combination of multi-scale convolution branches. We show the effectiveness of the CS-KiT with experimental results, outperforming state-of-the-art restoration approaches on image denoising, deblurring, and deraining benchmarks. © 2013 IEEE.
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