  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, this work provides 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.
