While some studies have proven that Swin Transformer (Swin) with window self-attention
(WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad …

Vision Transformers have shown promising performance in image restoration, which usually
conduct window-or channel-based attention to avoid intensive computations. Although the …

Convolutional neural network (CNN) has achieved great success on image super-resolution
(SR). However, most deep CNN-based SR models take massive computations to obtain …

Transformer-based methods have demonstrated impressive results on single-image super-
resolution (SISR) task. However, self-attention mechanism is computationally expensive …

While Transformer has achieved remarkable performance in various high-level vision tasks,
it is still challenging to exploit the full potential of Transformer in image restoration. The crux …

Recently, Transformer-based methods have shown impressive performance in single image
super-resolution (SISR) tasks due to the ability of global feature extraction. However, the …

Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …

Single image super-resolution (SISR) has made a significant breakthrough benefiting from
the prevalent rise of deep neural networks and large-scale training samples. The …

Limited by storage conditions, the degradation of old photos exhibits complex and diverse
features. Existing image restoration methods heavily rely on features extracted from a single …

The application of deep convolutional neural networks (CNNs) makes the lightweight single
image super-resolution (SISR) task develop rapidly in recent years. However, existing …