Although deep learning-based solutions have achieved impressive reconstruction
performance in image super-resolution (SR), these models are generally large, with …

Deep convolutional neural networks (CNNs) have pushed forward the frontier of super-
resolution (SR) research. However, current CNN models exhibit a major flaw: they are …

Deep learning based approaches has achieved great performance in single image super-
resolution (SISR). However, recent advances in efficient super-resolution focus on reducing …

While lightweight ViT framework has made tremendous progress in image super-resolution,
its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme …

Abstract Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision
aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional …

Super-resolution (SR) has achieved great success due to the development of deep
convolutional neural networks (CNNs). However, as the depth and width of the networks …

Runtime and memory consumption are two important aspects for efficient image super-
resolution (EISR) models to be deployed on resource-constrained devices. Recent …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …

Convolutional neural networks have allowed remarkable advances in single image super-
resolution (SISR) over the last decade. Among recent advances in SISR, attention …

Transformer-based methods have exhibited remarkable potential in single image super-
resolution (SISR) by effectively extracting long-range dependencies. However, most of the …