Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

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 …

Transformer-based methods have shown impressive performance in low-level vision tasks,
such as image super-resolution. However, we find that these networks can only utilize a …

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …

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

We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3
adapts denoising diffusion probabilistic models (Ho et al. 2020),(Sohl-Dickstein et al. 2015) …

Recent advances in single image super-resolution (SISR) have achieved extraordinary
performance, but the computational cost is too heavy to apply in edge devices. To alleviate …