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 2241–2250 of 3874 papers

TitleStatusHype
Frequency-Time Diffusion with Neural Cellular Automata—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
From General to Specific: Online Updating for Blind Super-Resolution—0
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks—0
Functional Neural Networks for Parametric Image Restoration Problems—0
Functional Nonlinear Sparse Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified