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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1761–1770 of 3874 papers

TitleStatusHype
A mathematical theory of super-resolution and two-point resolution—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images—0
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
A mathematical theory of resolution limits for super-resolution of positive sources—0
Deep Spectral Prior—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement—0
Show:102550
← PrevPage 177 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified