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 2281–2290 of 3874 papers

TitleStatusHype
Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction—0
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Temporal Consistency Learning of inter-frames for Video Super-ResolutionCode0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
VIINTER: View Interpolation with Implicit Neural Representations of Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified