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 3561–3570 of 3874 papers

TitleStatusHype
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New BenchmarkCode0
Projected Distribution Loss for Image EnhancementCode0
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimationCode0
Super-Resolving Face Image by Facial Parsing InformationCode0
Joint Super-Resolution and Alignment of Tiny FacesCode0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
Super-resolving Real-world Image Illumination Enhancement: A New Dataset and A Conditional Diffusion ModelCode0
Provably Convergent Plug-and-Play Quasi-Newton MethodsCode0
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying KernelsCode0
Joint Reconstruction and Spatial Super-Resolution of Hyper-Spectral CTIS Images via Multi-Scale RefinementCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified