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

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Showing 2101–2150 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
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—0
LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4RTX 4090s—0
Deconvolution with a Box—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction—0
Light field super resolution through controlled micro-shifts of light field sensor—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
Light Field Super-Resolution Via Graph-Based Regularization—0
Light Field Super-Resolution With Zero-Shot Learning—0
Light Stage Super-Resolution: Continuous High-Frequency Relighting—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
Debiased Subjective Assessment of Real-World Image Enhancement—0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—0
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation—0
XAI-based gait analysis of patients walking with Knee-Ankle-Foot orthosis using video cameras—0
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration—0
DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Lightweight single-image super-resolution network based on dual paths—0
Data-Driven Design for Fourier Ptychographic Microscopy—0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified