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 1526–1550 of 3874 papers

TitleStatusHype
Generalized super-resolution 4D Flow MRI x2013 using ensemble learning to extend across the cardiovascular systemCode0
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual InferenceCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse CodingCode0
Edge-Informed Single Image Super-ResolutionCode0
Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-ResolutionCode0
Remote Sensing Image Fusion Based on Two-stream Fusion NetworkCode0
Generative adversarial network-based image super-resolution using perceptual content lossesCode0
Image Super-resolution via Feature-augmented Random ForestCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical RectificationCode0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Image Super-Resolution Using a Wavelet-based Generative Adversarial NetworkCode0
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancementCode0
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias ClassificationCode0
Image Super-Resolution Using Dense Skip ConnectionsCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Image Super-Resolution by Neural Texture TransferCode0
EarthGen: Generating the World from Top-Down ViewsCode0
Image Super-Resolution Improved by Edge InformationCode0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified