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

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Showing 1201–1210 of 1589 papers

Title	Date	Tasks	Status
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity	Feb 13, 2019	Compressive SensingImage Super-Resolution	—Unverified
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks	Jul 3, 2019	Earth ObservationImage Super-Resolution	—Unverified
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network	Mar 27, 2024	Image Super-ResolutionSSIM	—Unverified
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization	Jan 23, 2016	Hyperspectral Image Super-ResolutionImage Super-Resolution	—Unverified
Super-resolution Reconstruction of Single Image for Latent features	Nov 16, 2022	DenoisingDiversity	—Unverified
Super-resolution Using Constrained Deep Texture Synthesis	Jan 26, 2017	Image Super-ResolutionSuper-Resolution	—Unverified
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation	Oct 2, 2018	Image Super-ResolutionSuper-Resolution	—Unverified
Super-Resolution with Deep Adaptive Image Resampling	Dec 18, 2017	Image Super-ResolutionSuper-Resolution	—Unverified
Super-resolved multi-temporal segmentation with deep permutation-invariant networks	Apr 6, 2022	Image ReconstructionImage Super-Resolution	—Unverified
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net	Jun 2, 2022	Image Super-ResolutionSuper-Resolution	—Unverified

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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