Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1221–1230 of 1589 papers

Title	Date	Tasks	Status	Hype
AeroRIT: A New Scene for Hyperspectral Image Analysis	Dec 17, 2019	Hyperspectral image analysisImage Super-Resolution	CodeCode Available	1
Spatial-Angular Interaction for Light Field Image Super-Resolution	Dec 17, 2019	Image Super-ResolutionSSIM	CodeCode Available	1
Image Processing Using Multi-Code GAN Prior	Dec 15, 2019	Blind Face RestorationColorization	CodeCode Available	0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features	Dec 9, 2019	Computational EfficiencyImage Super-Resolution	—Unverified	0
Explorable Super Resolution	Dec 4, 2019	Image Super-ResolutionSuper-Resolution	CodeCode Available	0
Texture Hallucination for Large-Factor Painting Super-Resolution	Dec 1, 2019	4kHallucination	—Unverified	0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers	Nov 30, 2019	Image Super-ResolutionSuper-Resolution	CodeCode Available	0
Deep Decomposition Learning for Inverse Imaging Problems	Nov 25, 2019	Compressive SensingImage Super-Resolution	CodeCode Available	0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images	Nov 25, 2019	Image Super-ResolutionOptical Character Recognition (OCR)	—Unverified	0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution	Nov 25, 2019	Generative Adversarial NetworkImage Super-Resolution	CodeCode Available	0

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All datasets Set14 - 4x upscaling BSD100 - 4x upscaling Urban100 - 4x upscaling Manga109 - 4x upscaling Set5 - 2x upscaling Set14 - 2x upscaling Set5 - 3x upscaling BSD100 - 2x upscaling Urban100 - 2x upscaling Set14 - 3x upscaling Urban100 - 3x upscaling BSD100 - 3x upscaling

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DRCT-L	PSNR	29.54	—	Unverified
2	HMA†	PSNR	29.51	—	Unverified
3	Hi-IR-L	PSNR	29.49	—	Unverified
4	HAT-L	PSNR	29.47	—	Unverified
5	HAT_FIR	PSNR	29.44	—	Unverified
6	DRCT	PSNR	29.4	—	Unverified
7	HAT	PSNR	29.38	—	Unverified
8	CPAT+	PSNR	29.36	—	Unverified
9	SwinFIR	PSNR	29.36	—	Unverified
10	CPAT	PSNR	29.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRCT-L	PSNR	28.16	—	Unverified
2	HMA†	PSNR	28.13	—	Unverified
3	Hi-IR-L	PSNR	28.13	—	Unverified
4	HAT-L	PSNR	28.09	—	Unverified
5	HAT_FIR	PSNR	28.07	—	Unverified
6	CPAT+	PSNR	28.06	—	Unverified
7	DRCT	PSNR	28.06	—	Unverified
8	HAT	PSNR	28.05	—	Unverified
9	CPAT	PSNR	28.04	—	Unverified
10	SwinFIR	PSNR	28.03	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hi-IR-L	PSNR	28.72	—	Unverified
2	DRCT-L	PSNR	28.7	—	Unverified
3	HMA†	PSNR	28.69	—	Unverified
4	HAT-L	PSNR	28.6	—	Unverified
5	HAT_FIR	PSNR	28.43	—	Unverified
6	DRCT	PSNR	28.4	—	Unverified
7	HAT	PSNR	28.37	—	Unverified
8	CPAT+	PSNR	28.33	—	Unverified
9	CPAT	PSNR	28.22	—	Unverified
10	PFT	PSNR	28.2	—	Unverified