Training Transformer Models by Wavelet Losses Improves Quantitative and
  Visual Performance in Single Image Super-Resolution
Cansu Korkmaz A. Murat Tekalp
Transformer-based models have achieved remarkable results in low-level vision
tasks including image super-resolution (SR). However, early Transformer-based
approaches that rely on self-attention within non-overlapping windows encounter
challenges in acquiring global information. To activate more input pixels
globally, hybrid attention models have been proposed. Moreover, training by
solely minimizing pixel-wise RGB losses, such as L1, have been found inadequate
for capturing essential high-frequency details. This paper presents two
contributions: i) We introduce convolutional non-local sparse attention (NLSA)
blocks to extend the hybrid transformer architecture in order to further
enhance its receptive field. ii) We employ wavelet losses to train Transformer
models to improve quantitative and subjective performance. While wavelet losses
have been explored previously, showing their power in training
Transformer-based SR models is novel. Our experimental results demonstrate that
the proposed model provides state-of-the-art PSNR results as well as superior
visual performance across various benchmark datasets.