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 291–300 of 3874 papers

TitleStatusHype
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Activating Wider Areas in Image Super-ResolutionCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Degradation Oriented and Regularized Network for Blind Depth Super-ResolutionCode1
A heterogeneous group CNN for image super-resolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified