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 1231–1240 of 3874 papers

TitleStatusHype
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Attention Based Real Image RestorationCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Masked Autoencoders are PDE LearnersCode0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRICode0
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified