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 626–650 of 1589 papers

TitleStatusHype
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
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