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 1881–1890 of 3874 papers

TitleStatusHype
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video—0
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
Delta-WKV: A Novel Meta-in-Context Learner for MRI Super-Resolution—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Facial Attribute Capsules for Noise Face Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified