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 11011125 of 1589 papers

TitleStatusHype
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution0
Deep Image Super Resolution via Natural Image Priors0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion0
Deep Artifact-Free Residual Network for Single Image Super-Resolution0
Perception-Oriented Stereo Image Super-Resolution0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow0
Perceptual Fairness in Image Restoration0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution0
Perceptual Video Super Resolution with Enhanced Temporal Consistency0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics0
CUF: Continuous Upsampling Filters0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery0
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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
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
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