Deep neural networks with a massive number of layers have made a remarkable
breakthrough on single image super-resolution (SR), but sacrifice computation complexity …

Conventional supervised super-resolution (SR) approaches are trained with massive
external SR datasets but fail to exploit desirable properties of the given test image. On the …

We propose an effective lightweight dynamic local and global self-attention network
(DLGSANet) to solve image super-resolution. Our method explores the properties of …

Many super-resolution (SR) models are optimized for high performance only and therefore
lack efficiency due to large model complexity. As large models are often not practical in real …

Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but
often require sophisticated architectures with heavy memory cost and computational …

Interactive image restoration aims to restore images by adjusting several controlling
coefficients, which determine the restoration strength. Existing methods are restricted in …

Deep convolutional neural networks (CNNs) are achieving great successes for image super-
resolution (SR), where global context is crucial for accurate restoration. However, the basic …

Fusing a low-resolution hyperspectral image (LrHSI) with an auxiliary high-resolution
multispectral image (HrMSI) is a burgeoning technique to realize hyperspectral image super …

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the
spotlight. However, most SR models were optimized with dated training strategies. In this …