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 591–600 of 3874 papers

TitleStatusHype
ChartEye: A Deep Learning Framework for Chart Information Extraction—0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment—0
Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-ResolutionCode0
A Preliminary Exploration Towards General Image Restoration—0
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
Cascaded Temporal Updating Network for Efficient Video Super-ResolutionCode1
Particle-Filtering-based Latent Diffusion for Inverse Problems—0
FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss—0
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral ImagesCode0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified