A comprehensive experiment-based review of low-light image enhancement methods and benchmarking low-light image quality assessment
Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …
Blind image quality assessment via vision-language correspondence: A multitask learning perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …
perception of image quality without any reference information. We develop a general and …
Exploring clip for assessing the look and feel of images
Measuring the perception of visual content is a long-standing problem in computer vision.
Many mathematical models have been developed to evaluate the look or quality of an …
Many mathematical models have been developed to evaluate the look or quality of an …
Maniqa: Multi-dimension attention network for no-reference image quality assessment
Abstract No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual
quality of images in accordance with human subjective perception. Unfortunately, existing …
quality of images in accordance with human subjective perception. Unfortunately, existing …
Designing a practical degradation model for deep blind image super-resolution
It is widely acknowledged that single image super-resolution (SISR) methods would not
perform well if the assumed degradation model deviates from those in real images. Although …
perform well if the assumed degradation model deviates from those in real images. Although …
Details or artifacts: A locally discriminative learning approach to realistic image super-resolution
Single image super-resolution (SISR) with generative adversarial networks (GAN) has
recently attracted increasing attention due to its potentials to generate rich details. However …
recently attracted increasing attention due to its potentials to generate rich details. However …
A review of single image super-resolution reconstruction based on deep learning
M Yu, J Shi, C Xue, X Hao, G Yan - Multimedia Tools and Applications, 2024 - Springer
Single image super-resolution (SISR) is an important research field in computer vision, the
purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) …
purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) …
Masked image training for generalizable deep image denoising
When capturing and storing images, devices inevitably introduce noise. Reducing this noise
is a critical task called image denoising. Deep learning has become the de facto method for …
is a critical task called image denoising. Deep learning has become the de facto method for …
Efficient image super-resolution using vast-receptive-field attention
The attention mechanism plays a pivotal role in designing advanced super-resolution (SR)
networks. In this work, we design an efficient SR network by improving the attention …
networks. In this work, we design an efficient SR network by improving the attention …
Interpreting super-resolution networks with local attribution maps
Image super-resolution (SR) techniques have been developing rapidly, benefiting from the
invention of deep networks and its successive breakthroughs. However, it is acknowledged …
invention of deep networks and its successive breakthroughs. However, it is acknowledged …