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 1251–1275 of 3874 papers

TitleStatusHype
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-ResolutionCode0
Medical Image Imputation from Image CollectionsCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Masked Autoencoders are PDE LearnersCode0
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CTCode0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-ResolutionCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachCode0
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Cross-Resolution Learning for Face RecognitionCode0
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale WarpingCode0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Cross-domain heterogeneous residual network for single image super-resolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified