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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2351–2375 of 3874 papers

TitleStatusHype
Memory-Augmented Non-Local Attention for Video Super-ResolutionCode1
edge-SR: Super-Resolution For The MassesCode1
SwinIR: Image Restoration Using Swin TransformerCode3
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Thermal Image Processing via Physics-Inspired Deep NetworksCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Temporal Kernel Consistency for Blind Video Super-Resolution—0
Light Field Image Super-Resolution with TransformersCode1
spectrai: A deep learning framework for spectral dataCode1
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Efficient Light Field Reconstruction via Spatio-Angular Dense NetworkCode0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
Finding Discriminative Filters for Specific Degradations in Blind Super-ResolutionCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
Improving Multi-View Stereo via Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified