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 3651–3660 of 3874 papers

TitleStatusHype
Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-DesignCode0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
DFU: scale-robust diffusion model for zero-shot super-resolution image generationCode0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Detecting Overfitting of Deep Generative Networks via Latent RecoveryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified