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- W4379806295 abstract "As multimedia content often contains noise from intrinsic defects of digital devices, image denoising is an important step for high-level vision recognition tasks. Although several studies have developed the denoising field employing advanced Transformers, these networks are too momory-intensive for real-world applications. Additionally, there is a lack of research on lightweight denosing (LWDN) with Transformers. To handle this, we provide seven comparative baseline Transformers for LWDN, serving as a foundation for future research. We also demonstrate the parts of randomly cropped patches significantly affect the denoising performances during training. While previous studies have overlooked this aspect, we aim to train our baseline Transformers in a truly fair manner. Furthermore, we conduct empirical analyses of various components to determine the key considerations for constructing LWDN Transformers. Codes are available at https://github.com/rami0205/LWDN." @default.
- W4379806295 created "2023-06-09" @default.
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- W4379806295 date "2023-06-12" @default.
- W4379806295 modified "2023-09-27" @default.
- W4379806295 title "Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training" @default.
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- W4379806295 doi "https://doi.org/10.1145/3591106.3592265" @default.
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