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 1591–1600 of 3874 papers

TitleStatusHype
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Learning Continuous Mesh Representation with Spherical Implicit SurfaceCode1
Deep Residual Axial Networks—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—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
WIRE: Wavelet Implicit Neural RepresentationsCode1
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
Super-resolution with Binary Priors: Theory and Algorithms—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified