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 2481–2490 of 3874 papers

TitleStatusHype
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution—0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution—0
Novel Super-Resolution Method Based on High Order Nonlocal-Means—0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning—0
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results—0
CNN-based synthesis of realistic high-resolution LiDAR data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified