Image Enhancement
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Greatest papers with code
Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs
CVPR 2018
• nothinglo/Deep-Photo-Enhancer
• 
First, we augment the U-Net with global features and show that it is more effective.
EnlightenGAN: Deep Light Enhancement without Paired Supervision
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?
Getting to Know Low-light Images with The Exclusively Dark Dataset
Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i. e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.
Deep Retinex Decomposition for Low-Light Enhancement
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.
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks
ECCV2018 2018
• thangvubk/FEQE
•
This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues.
An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging
Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium.
Image Super-Resolution as a Defense Against Adversarial Attacks
• aamir-mustafa/super-resolution-adversarial-defense
• 
The proposed scheme is simple and has the following advantages: (1) it does not require any model training or parameter optimization, (2) it complements other existing defense mechanisms, (3) it is agnostic to the attacked model and attack type and (4) it provides superior performance across all popular attack algorithms.
ADVERSARIAL DEFENSE IMAGE ENHANCEMENT IMAGE RESTORATION IMAGE SUPER-RESOLUTION
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement.
SOTA for Image Cropping on AVA (using extra training data)
Single Image Reflection Separation with Perceptual Losses
CVPR 2018
• vinthony/ghost-free-shadow-removal
•
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.
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets
X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions.
IMAGE ENHANCEMENT MEDICAL X-RAY IMAGE SEGMENTATION SEMANTIC SEGMENTATION