SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1875 of 3874 papers

TitleStatusHype
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
In-situ monitoring additive manufacturing process with AI edge computing—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
Image Neural Field Diffusion Models—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Interactive Image Manipulation with Complex Text Instructions—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
Image inpainting for corrupted images by using the semi-super resolution GAN—0
Interpretable Deep Multimodal Image Super-Resolution—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
Interpretable Super-Resolution via a Learned Time-Series Representation—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Image Deconvolution with Deep Image and Kernel Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified