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 201–210 of 3874 papers

TitleStatusHype
Burstormer: Burst Image Restoration and Enhancement TransformerCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified