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 1481–1490 of 3874 papers

TitleStatusHype
High-Frequency aware Perceptual Image Enhancement—0
From General to Specific: Online Updating for Blind Super-Resolution—0
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes—0
CUF: Continuous Upsampling Filters—0
A GPU-Accelerated Light-field Super-resolution Framework Based on Mixed Noise Model and Weighted Regularization—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified