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

Showing 301325 of 1589 papers

TitleStatusHype
Single-sample image-fusion upsampling of fluorescence lifetime images0
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-ResolutionCode2
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge ReportCode3
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolutionCode1
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model0
NTIRE 2024 Challenge on Image Super-Resolution (4): Methods and ResultsCode1
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution0
Differentiable Search for Finding Optimal Quantization Strategy0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures0
LIPT: Latency-aware Image Processing TransformerCode1
Efficient Learnable Collaborative Attention for Single Image Super-Resolution0
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks0
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network0
A Study in Dataset Pruning for Image Super-Resolution0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution0
CFAT: Unleashing TriangularWindows for Image Super-resolutionCode2
Efficient scene text image super-resolution with semantic guidanceCode1
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Benchmark Results

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