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 726–750 of 3874 papers

TitleStatusHype
Gradient Step Denoiser for convergent Plug-and-PlayCode1
Burst Image Restoration and EnhancementCode1
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNNCode1
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
Structure-Preserving Image Super-ResolutionCode1
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical SystemsCode1
Exploring Separable Attention for Multi-Contrast MR Image Super-ResolutionCode1
Dual-Camera Super-Resolution with Aligned Attention ModulesCode1
Self-Attention for Audio Super-ResolutionCode1
Generalized Real-World Super-Resolution through Adversarial RobustnessCode1
Memory-Augmented Non-Local Attention for Video Super-ResolutionCode1
Transformer for Single Image Super-ResolutionCode1
edge-SR: Super-Resolution For The MassesCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Thermal Image Processing via Physics-Inspired Deep NetworksCode1
spectrai: A deep learning framework for spectral dataCode1
Light Field Image Super-Resolution with TransformersCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
Finding Discriminative Filters for Specific Degradations in Blind Super-ResolutionCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
Self-Conditioned Probabilistic Learning of Video RescalingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified