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 2121–2130 of 3874 papers

TitleStatusHype
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations—0
LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction—0
License Plate Super-Resolution Using Diffusion Models—0
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—0
LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4RTX 4090s—0
Deconvolution with a Box—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified