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 1271–1280 of 3874 papers

TitleStatusHype
StarSRGAN: Improving Real-World Blind Super-ResolutionCode1
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid SubjectsCode0
Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architectureCode0
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
A full-resolution training framework for Sentinel-2 image fusion—0
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting—0
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolutionCode1
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified