Image Enhancement

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 801–810 of 983 papers

Title	Date	Tasks	Status	Hype
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network	Mar 20, 2020	Generative Adversarial NetworkImage Enhancement	CodeCode Available	1
Burst Denoising of Dark Images	Mar 17, 2020	DenoisingImage Enhancement	CodeCode Available	1
A CNN-Based Blind Denoising Method for Endoscopic Images	Mar 16, 2020	DenoisingImage Enhancement	CodeCode Available	1
Learning Enriched Features for Real Image Restoration and Enhancement	Mar 15, 2020	DenoisingImage Denoising	CodeCode Available	1
Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification	Mar 14, 2020	Cancer ClassificationGeneral Classification	—Unverified	0
Self-supervised Image Enhancement Network: Training with Low Light Images Only	Feb 26, 2020	Image EnhancementLow-Light Image Enhancement	CodeCode Available	1
Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement	Feb 20, 2020	Domain AdaptationImage Enhancement	—Unverified	0
SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression	Feb 15, 2020	DecoderDenoising	—Unverified	0
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception	Feb 4, 2020	Image EnhancementSaliency Prediction	CodeCode Available	1
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter	Feb 3, 2020	Image Enhancement	CodeCode Available	1

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All datasets MIT-Adobe 5k TIP 2018 Exposure-Errors MIT-Adobe FiveK SICE-Grad SICE-Mix

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	HG-MTFE	PSNR on proRGB	25.69	—	Unverified
2	PQDynamicISP	PSNR on proRGB	25.53	—	Unverified
3	RSFNet-map	PSNR on proRGB	25.49	—	Unverified
4	AdaInt	PSNR on proRGB	25.49	—	Unverified
5	SepLUT	PSNR on proRGB	25.47	—	Unverified
6	MTFE	PSNR on proRGB	25.46	—	Unverified
7	3D LUT	PSNR on proRGB	25.21	—	Unverified
8	Retinexformer	PSNR on sRGB	24.94	—	Unverified
9	4D LUT	PSNR on proRGB	24.61	—	Unverified
10	DIFAR (MSCA, level 1)	PSNR on proRGB	24.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ESDNet-L	PSNR	30.11	—	Unverified
2	MBCNN	PSNR	30.03	—	Unverified
3	ESDNet	PSNR	29.81	—	Unverified
4	Uformer-B	PSNR	29.28	—	Unverified
5	MopNet	PSNR	27.75	—	Unverified
6	DMCNN	PSNR	26.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Exposure-slot	PSNR	23.18	—	Unverified
2	CSEC	PSNR	22.73	—	Unverified
3	LCDPNet	PSNR	22.17	—	Unverified
4	IAT	PSNR	20.34	—	Unverified
5	MSEC	PSNR	20.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TreEnhance	DeltaE	11.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CIDNet	Average PSNR	13.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CIDNet	Average PSNR	13.43	—	Unverified