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 1061–1070 of 3874 papers

TitleStatusHype
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration—0
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime—0
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency—0
An Attention-Based Approach for Single Image Super Resolution—0
Diffusion Posterior Sampling is Computationally Intractable—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Anatomically Guided Motion Correction for Placental IVIM Parameter Estimation with Accelerated Sampling Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified