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 1901–1925 of 3874 papers

TitleStatusHype
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network—0
PTSR: Patch Translator for Image Super-Resolution—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution—0
HSTR-Net: Reference Based Video Super-resolution with Dual Cameras—0
Video Super-Resolution Using a Grouped Residual in Residual Network—0
Super resolution of histopathological frozen sections via deep learning preserving tissue structure—0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing—0
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition—0
FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion—0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling—0
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation—0
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution—0
Prompt-tuning latent diffusion models for inverse problems—0
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
A Restoration Network as an Implicit Prior—0
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
A New Real-World Video Dataset for the Comparison of Defogging Algorithms—0
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified