In this report we summarize the 2nd NTIRE challenge on light field (LF) image super-
resolution (SR) with a focus on new methods and results. This challenge aims at super …

Transformer-based models have been widely and successfully used in various low-vision
visual tasks, and have achieved remarkable performance in single image super-resolution …

Recently developed generative methods, including invertible rescaling network (IRN) based
and generative adversarial network (GAN) based methods, have demonstrated exceptional …

Inspired by the great success of the Transformer in computer vision, some works have
started to explore the use of the Transformer for super-resolution (SR). However, with regard …

Land Surface Temperature (LST) is a critical parameter for environmental studies, but
obtaining high-resolution LST data remains challenging due to the spatio-temporal trade-off …

Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable
achievements in various image processing tasks like image rescaling. However, we observe …

Recently, the quality of generated images in image super-resolution (SR) has significantly
improved due to the widespread application of convolutional neural networks. Existing super …

The emergence of convolutional neural network (CNN) and transformer has recently
facilitated significant advances in image super-resolution (SR) tasks. However, these …

The presence of light absorption and scattering in underwater conditions results in
underwater images with missing details, low contrast, and color bias. The current deep …

To address the issues of detail loss, natural texture distortion, small receptive field, overly
smooth reconstructed images, and the limitations of single-scale convolution kernels in …