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Greatest papers with code

EnlightenGAN: Deep Light Enhancement without Paired Supervision

17 Jun 2019kritiksoman/GIMP-ML

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?

IMAGE RESTORATION LOW-LIGHT IMAGE ENHANCEMENT

Deep Retinex Decomposition for Low-Light Enhancement

14 Aug 2018weichen582/RetinexNet

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.

DENOISING LOW-LIGHT IMAGE ENHANCEMENT

Getting to Know Low-light Images with The Exclusively Dark Dataset

29 May 2018cs-chan/Exclusively-Dark-Image-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.

LOW-LIGHT IMAGE ENHANCEMENT OBJECT DETECTION

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

CVPR 2020 Li-Chongyi/Zero-DCE

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.

FACE DETECTION LOW-LIGHT IMAGE ENHANCEMENT

Kindling the Darkness: A Practical Low-light Image Enhancer

4 May 2019zhangyhuaee/KinD

It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information.

LOW-LIGHT IMAGE ENHANCEMENT

Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset

2 Aug 2019Lvfeifan/MBLLEN

Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark.

 Ranked #1 on Low-Light Image Enhancement on 3DMatch Benchmark (using extra training data)

DENOISING LOW-LIGHT IMAGE ENHANCEMENT

Self-supervised Image Enhancement Network: Training with Low Light Images Only

26 Feb 2020hitzhangyu/Self-supervised-Image-Enhancement-Network-Training-With-Low-Light-Images-Only

We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning.

LOW-LIGHT IMAGE ENHANCEMENT SELF-SUPERVISED LEARNING

LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement

12 Nov 2015kglore/llnet_color

In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation.

DENOISING LOW-LIGHT IMAGE ENHANCEMENT

Learning an Adaptive Model for Extreme Low-light Raw Image Processing

22 Apr 2020505030475/ExtremeLowLight

Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.

DENOISING LOW-LIGHT IMAGE ENHANCEMENT SSIM