The problem of blind image super-resolution aims to recover high-resolution (HR) images
from low-resolution (LR) images with unknown degradation modes. Most existing methods …

Video Large Language Models (Vid-LLMs) have made remarkable advancements in
comprehending video content for QA dialogue. However, they struggle to extend this visual …

This paper proposes a super-resolution algorithm for reconstructing license plate images
based on generative adversarial networks (GAN) for improving the recognition rate of low …

Scene text image super-resolution (STISR) aims at simultaneously increasing the resolution
and readability of low-resolution scene text images, thus boosting the performance of the …

Single image super‐resolution (SISR) is a promising research direction in computer vision
and image processing for improving the visual perception of low‐quality images. In recent …

Scene text image super-resolution (STISR) has obtained widespread attention in recent
years due to its ability to enhance text recognition performance. Many previous methods …

Recently, Transformer-based techniques have demonstrated impressive effectiveness
across various high-and low-level vision tasks by leveraging the self-attention mechanism …

Scene text image super-resolution (STISR) aims to enhance the resolution of text images
while simultaneously improving their readability by reducing noise, blur, and other …

In this letter, we developed novel Structure Enhanced Channel Attention Network
(SECANet) for scene text image super‐resolution (STISR). The newly proposed SECANet …

Scene Text Image Super-Resolution (STISR) aims to simultaneously improve the resolution
and legibility of text images, generating images that will significantly impact the performance …