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 1226–1250 of 1589 papers

TitleStatusHype
Test-time Training for Hyperspectral Image Super-resolution—0
Text-guided Explorable Image Super-resolution—0
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance—0
Texture-Based Error Analysis for Image Super-Resolution—0
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution—0
Texture Hallucination for Large-Factor Painting Super-Resolution—0
Medical Image Super-Resolution Using a Generative Adversarial Network—0
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control—0
The Perception-Robustness Tradeoff in Deterministic Image Restoration—0
The Power of Context: How Multimodality Improves Image Super-Resolution—0
Time accelerated image super-resolution using shallow residual feature representative network—0
Time-lapse image classification using a diffractive neural network—0
Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models—0
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model—0
Toward Real-World Super-Resolution via Adaptive Downsampling Models—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution—0
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Towards Realistic Data Generation for Real-World Super-Resolution—0
Towards Robust Drone Vision in the Wild—0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
Toward task-driven satellite image super-resolution—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—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