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 2271–2280 of 3874 papers

TitleStatusHype
Transformer-Driven Inverse Problem Transform for Fast Blind Hyperspectral Image Dehazing—0
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
MFSR-GAN: Multi-Frame Super-Resolution with Handheld Motion Modeling—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Continuous Sign Language Recognition via Temporal Super-Resolution Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified