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 2251–2275 of 3874 papers

TitleStatusHype
FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion—0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution—0
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content—0
GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising—0
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems—0
GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans—0
Gauging diffraction patterns: field of view and bandwidth estimation in lensless holography—0
GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors—0
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution—0
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Gemino: Practical and Robust Neural Compression for Video Conferencing—0
GenDR: Lightning Generative Detail Restorator—0
Generalizable One-shot Neural Head Avatar—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
OFDM Reference Signal Pattern Design Criteria for Integrated Communication and Sensing—0
Generating Unobserved Alternatives—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Generative adversarial network for super-resolution imaging through a fiber—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified