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 3451–3460 of 3874 papers

TitleStatusHype
cGANs with Projection DiscriminatorCode0
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient SimulationsCode0
CFSNet: Toward a Controllable Feature Space for Image RestorationCode0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional NetworkCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified