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

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Showing 601–650 of 1589 papers

TitleStatusHype
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution—0
Image Super-Resolution With Deep Variational Autoencoders—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
A Dive into SAM Prior in Image Restoration—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment—0
A Reverse Hierarchy Model for Predicting Eye Fixations—0
A Convex Approach for Image Hallucination—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Improving Generative Adversarial Networks for Video Super-Resolution—0
A Comprehensive Survey of Transformers for Computer Vision—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution—0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—0
Cascade Convolutional Neural Network for Image Super-Resolution—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
A deep primal-dual proximal network for image restoration—0
Double Sparse Multi-Frame Image Super Resolution—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Perceptual Image Super-Resolution with Progressive Adversarial Network—0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
Distilling with Residual Network for Single Image Super Resolution—0
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales—0
Why Are Deep Representations Good Perceptual Quality Features?—0
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models—0
Burst Image Super-Resolution with Mamba—0
DISCO: Distributed Inference with Sparse Communications—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
Burst Image Super-Resolution with Base Frame Selection—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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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