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 2101–2125 of 3874 papers

TitleStatusHype
Learning to Generate Images with Perceptual Similarity Metrics—0
Learning to Have an Ear for Face Super-Resolution—0
Learning to Super-Resolve Blurry Face and Text Images—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Toward task-driven satellite image super-resolution—0
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery—0
Learning To Zoom Inside Camera Imaging Pipeline—0
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling—0
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
Depth-Independent Depth Completion via Least Square Estimation—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network—0
Lessons Learned Report: Super-Resolution for Detection Tasks in Engineering Problem-Solving—0
Level generation and style enhancement -- deep learning for game development overview—0
Leveraging Land Cover Priors for Isoprene Emission Super-Resolution—0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data—0
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations—0
LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction—0
License Plate Super-Resolution Using Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified