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 2876–2900 of 3874 papers

TitleStatusHype
Training a Task-Specific Image Reconstruction Loss—0
Training Set Effect on Super Resolution for Automated Target Recognition—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms—0
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches—0
Transformer-Driven Inverse Problem Transform for Fast Blind Hyperspectral Image Dehazing—0
Transformers in Vision: A Survey—0
Translation-based Video-to-Video Synthesis—0
Translation position extracting in incoherent Fourier ptychography—0
Transport-based analysis, modeling, and learning from signal and data distributions—0
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images—0
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients—0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
TriNeRFLet: A Wavelet Based Triplane NeRF Representation—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
Trustworthy modelling of atmospheric formaldehyde powered by deep learning—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond—0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks—0
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified