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 2191–2200 of 3874 papers

TitleStatusHype
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics—0
Super-Resolution Harmonic Retrieval of Non-Circular Signals—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Deep Residual Axial Networks—0
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging—0
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
Super-resolution with Sparse Arrays: A Non-Asymptotic Analysis of Spatio-temporal Trade-offs—0
Show:102550
← PrevPage 220 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified