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 3476–3500 of 3874 papers

TitleStatusHype
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation—0
VoLUT: Efficient Volumetric streaming enhanced by LUT-based super-resolution—0
T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions—0
Aerial Spectral Super-Resolution using Conditional Adversarial Networks—0
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy—0
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network—0
Application of convolutional neural networks in image super-resolution—0
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision—0
VSpSR: Explorable Super-Resolution via Variational Sparse Representation—0
Task-Aware Image Downscaling—0
Task Decoupled Framework for Reference-Based Super-Resolution—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding—0
Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks—0
Enhancing Image Resolution: A Simulation Study and Sensitivity Analysis of System Parameters for Resourcesat-3S/3SA—0
Task-driven real-world super-resolution of document scans—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified