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 201–210 of 3874 papers

TitleStatusHype
Anatomically Guided Motion Correction for Placental IVIM Parameter Estimation with Accelerated Sampling Method—0
Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-ResolutionCode0
DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration—0
Variational Message Passing-based Multiobject Tracking for MIMO-Radars using Raw Sensor Signals—0
Toward task-driven satellite image super-resolution—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model—0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution—0
The Power of Context: How Multimodality Improves Image Super-Resolution—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified