SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1251–1275 of 1589 papers

TitleStatusHype
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
Unsupervised Learning for Real-World Super-Resolution—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Boomerang: Local sampling on image manifolds using diffusion models—0
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
BOLD: Boolean Logic Deep Learning—0
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization—0
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Self-Organized Residual Blocks for Image Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Blind Image Super-Resolution: A Survey and Beyond—0
Self-Tuned Deep Super Resolution—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
BLADE: Filter Learning for General Purpose Computational Photography—0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified