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 1461–1470 of 3874 papers

TitleStatusHype
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Frame-Recurrent Video Super-Resolution—0
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos—0
FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss—0
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform—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
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified