Eswindnet: Image Demoiréing Using Multiscale Swin Transformer Layers

Capturing electronic screens with digital cameras introduces high-frequency artifacts, known as moiré patterns, degrading overall image quality and colors. This work proposes ESwinDNet, an image demoiréing model that combines an encoder-decoder architecture with multiscale Swin Transformer layers. These layers efficiently compute pixel-level attention, a crucial aspect for low-level vision tasks such as image demoiréing. The proposed ESwinDNet model achieves comparable results to the large variant of the baseline model ESDNet-L on the UHDM dataset, demonstrating its capabilities in the removal of moiré patterns in 4K images, with nearly half the number of parameters and floating point operations, yielding faster training and inference time. The code is available on Github.