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 2051–2075 of 3874 papers

TitleStatusHype
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction—0
Learning From Unpaired Data: A Variational Bayes Approach—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
Learning Hierarchical Color Guidance for Depth Map Super-Resolution—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
Learning Implicit Generative Models by Matching Perceptual Features—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving—0
Deep learning at scale for subgrid modeling in turbulent flows—0
Towards the Automation of Deep Image Prior—0
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging—0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling—0
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks—0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
Deep Image Super Resolution via Natural Image Priors—0
MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks—0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution—0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified