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As multimedia content often contains noise from intrinsic defects of digitaldevices, image denoising is an important step for high-level vision recognitiontasks. Although several studies have developed the denoising field employingadvanced Transformers, these networks are too momory-intensive for real-worldapplications. Additionally, there is a lack of research on lightweight denosing(LWDN) with Transformers. To handle this, this work provides seven comparativebaseline Transformers for LWDN, serving as a foundation for future research. Wealso demonstrate the parts of randomly cropped patches significantly affect thedenoising performances during training. While previous studies have overlookedthis aspect, we aim to train our baseline Transformers in a truly fair manner.Furthermore, we conduct empirical analyses of various components to determinethe key considerations for constructing LWDN Transformers. Codes are availableat https://github.com/rami0205/LWDN.

Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training