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 2301–2325 of 3874 papers

TitleStatusHype
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency GuidanceCode0
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
A Regularized Conditional GAN for Posterior Sampling in Image Recovery ProblemsCode0
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
Efficient Hair Style Transfer with Generative Adversarial Networks—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Super-Resolution and Image Re-projection for Iris Recognition—0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying KernelsCode0
Video super-resolution for single-photon LIDAR—0
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution—0
Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering—0
ISTA-Inspired Network for Image Super-Resolution—0
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
CUF: Continuous Upsampling Filters—0
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-ResolutionCode0
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models—0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified