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Found 34 papers, 11 papers with code
A Spatio-temporal Aligned SUNet Model for Low-light Video Enhancement
Distortions caused by low-light conditions are not only visually unpleasant but also degrade the performance of computer vision tasks.
Feature Denoising For Low-Light Instance Segmentation Using Weighted Non-Local Blocks
Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast.
BVI-Lowlight: Fully Registered Benchmark Dataset for Low-Light Video Enhancement
Low-light videos often exhibit spatiotemporal incoherent noise, leading to poor visibility and compromised performance across various computer vision applications.
WavePlanes: A compact Wavelet representation for Dynamic Neural Radiance Fields
We propose a multi-scale space and space-time feature plane representation using N-level 2-D wavelet coefficients.
OCT2Confocal: 3D CycleGAN based Translation of Retinal OCT Images to Confocal Microscopy
This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy.
DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images
In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis.
Wavelet-based Topological Loss for Low-Light Image Denoising
Despite extensive research conducted in the field of image denoising, many algorithms still heavily depend on supervised learning and their effectiveness primarily relies on the quality and diversity of training data.
Towards a Robust Framework for NeRF Evaluation
Additionally, we propose a novel metric to measure task complexity of the framework which accounts for the visual parameters and the distribution of the spatial data.
ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation
Currently, one of the major challenges in deep learning-based video frame interpolation (VFI) is the large model sizes and high computational complexity associated with many high performance VFI approaches.
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images
Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment.
Object recognition in atmospheric turbulence scenes
The influence of atmospheric turbulence on acquired surveillance imagery poses significant challenges in image interpretation and scene analysis.
ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results
Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists.
A Topological Loss Function: Image Denoising on a Low-Light Dataset
The loss function is a combination of $\ell_1$ or $\ell_2$ losses with the new persistence-based topological loss.
Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Network
Most well-developed approaches to remove atmospheric turbulence distortion are model-based.
Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset
Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment.
High-resolution Coastline Extraction in SAR Images via MISP-GGD Superpixel Segmentation
High accuracy coastline/shoreline extraction from SAR imagery is a crucial step in a number of maritime and coastal monitoring applications.
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults.
Optimal Transport-based Graph Matching for 3D retinal OCT image registration
Finally, registration along the direction orthogonal to the x-y plane (the z-direction) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR).
Self-supervised Contrastive Learning for Volcanic Unrest Detection
Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption.
Unsupervised Image Fusion Using Deep Image Priors
The results are compared with state-of-the-art image fusion methods.
Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning
The traditional diagnosis usually relies on manual analysis from microscopic images which is prone to human error due to morphological similarity of different parasitic eggs and abundance of impurities in a sample.
Analysis of Vision-based Abnormal Red Blood Cell Classification
This paper presents an automated process utilising the advantages of machine learning to increase capacity and standardisation of cell abnormality detection, and its performance is analysed.
Current Advances in Computational Lung Ultrasound Imaging: A Review
In the field of biomedical imaging, ultrasonography has become increasingly widespread, and an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable.
Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset
The imbalance ratio of this dataset was 34. 538 for 12 RBC classes from 20, 875 individual RBC samples.
Artificial Intelligence in the Creative Industries: A Review
We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services.
Encoding in the Dark Grand Challenge: An Overview
In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment.
Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization
In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients.
Fast Depth Estimation for View Synthesis
Disparity/depth estimation from sequences of stereo images is an important element in 3D vision.
Towards automated mobile-phone-based plant pathology management
This paper presents a framework which uses computer vision algorithms to standardise images and analyse them for identifying crop diseases automatically.
Enhanced Spatially Interleaved Techniques for Multi-View Distributed Video Coding
This paper presents a multi-view distributed video coding framework for independent camera encoding and centralized decoding.
Image Fusion via Sparse Regularization with Non-Convex Penalties
The performance of the proposed method is benchmarked against a number of state-of-the-art image fusion techniques and superior performance is demonstrated both visually and in terms of various assessment measures.
A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging.
