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 571–580 of 983 papers

Title	Date	Tasks	Status	Hype
Learning Enriched Features for Fast Image Restoration and Enhancement	Apr 19, 2022	Autonomous VehiclesDeblurring	—Unverified	0
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics	Apr 19, 2022	Image EnhancementImage Quality Assessment	CodeCode Available	0
Underwater Image Enhancement Using Pre-trained Transformer	Apr 8, 2022	DenoisingImage Enhancement	—Unverified	0
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks	Apr 4, 2022	Image Enhancementobject-detection	—Unverified	0
Extremely Low-light Image Enhancement with Scene Text Restoration	Apr 1, 2022	Image EnhancementImage Restoration	CodeCode Available	0
MyStyle: A Personalized Generative Prior	Mar 31, 2022	Image EnhancementSuper-Resolution	—Unverified	0
Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis	Mar 29, 2022	Generative Adversarial NetworkImage Enhancement	CodeCode Available	1
Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstruction	Mar 23, 2022	CT ReconstructionImage Enhancement	—Unverified	0
Underwater Light Field Retention : Neural Rendering for Underwater Imaging	Mar 21, 2022	Image EnhancementImage Generation	CodeCode Available	1
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation	Mar 18, 2022	Image EnhancementMedical Image Analysis	—Unverified	0

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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	AdaInt	PSNR on proRGB	25.49	—	Unverified
4	RSFNet-map	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