Image Enhancement

216 papers with code • 4 benchmarks • 11 datasets

Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.

Source: A Comprehensive Review of Image Enhancement Techniques


Use these libraries to find Image Enhancement models and implementations
2 papers

Most implemented papers

Learning Enriched Features for Real Image Restoration and Enhancement

swz30/MIRNet ECCV 2020

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

Li-Chongyi/Zero-DCE CVPR 2020

The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.

EnlightenGAN: Deep Light Enhancement without Paired Supervision

yueruchen/EnlightenGAN 17 Jun 2019

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?

Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

xahidbuffon/Deep-SESR 4 Feb 2020

In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications.

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Jakaria08/Filter_Enhance_Detect 20 Mar 2020

Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance.

Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation

Li-Chongyi/Zero-DCE_extension 1 Mar 2021

This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.

Uformer: A General U-Shaped Transformer for Image Restoration

ZhendongWang6/Uformer CVPR 2022

Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.

Deep Burst Denoising

Ourshanabi/Burst-denoising ECCV 2018

One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance.

Single Image Reflection Separation with Perceptual Losses

ceciliavision/perceptual-reflection-removal CVPR 2018

Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers.

Deep Retinex Decomposition for Low-Light Enhancement

weichen582/RetinexNet 14 Aug 2018

Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance.