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 3401–3410 of 3874 papers

TitleStatusHype
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Deep Generative Model based Rate-Distortion for Image Downscaling AssessmentCode0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Super-resolution of Omnidirectional Images Using Adversarial LearningCode0
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral ImagesCode0
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
Toward Real-World Light Field Super-ResolutionCode0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified