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 1221–1230 of 3874 papers

TitleStatusHype
Deep Back-Projection Networks For Super-ResolutionCode0
Deep Back-Projection Networks for Single Image Super-resolutionCode0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
AIM 2019 Challenge on Constrained Super-Resolution: Methods and ResultsCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Masked Autoencoders are PDE LearnersCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Audio Super Resolution using Neural NetworksCode0
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified