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 3221–3230 of 3874 papers

TitleStatusHype
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
Image Formation Model Guided Deep Image Super-ResolutionCode0
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-ResolutionCode0
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement—0
A Tour of Convolutional Networks Guided by Linear InterpretersCode2
Super-resolution of Omnidirectional Images Using Adversarial LearningCode0
Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified