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 1291–1300 of 3874 papers

TitleStatusHype
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified