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 2771–2780 of 3874 papers

TitleStatusHype
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation—0
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution—0
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified