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 2561–2570 of 3874 papers

TitleStatusHype
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text ImagesCode0
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksCode0
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution—0
Image quality measurements and denoising using Fourier Ring CorrelationsCode0
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution—0
Sound and Visual Representation Learning with Multiple Pretraining Tasks—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization—0
Generative adversarial network for super-resolution imaging through a fiber—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified