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Image Enhancement

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Development and quantitative assessment of deep learning-based image enhancement for optical coherence tomography.

BMC ophthalmology
PURPOSE: To develop a deep learning-based framework to improve the image quality of optical coherence tomography (OCT) and evaluate its image enhancement effect with the traditional image averaging method from a clinical perspective.

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.

Journal of healthcare engineering
In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated w...

A Hemolysis Image Detection Method Based on GAN-CNN-ELM.

Computational and mathematical methods in medicine
Since manual hemolysis test methods are given priority with practical experience and its cost is high, the characteristics of hemolysis images are studied. A hemolysis image detection method based on generative adversarial networks (GANs) and convolu...

Detection and identification of tea leaf diseases based on AX-RetinaNet.

Scientific reports
The accurate detection and identification of tea leaf diseases are conducive to its precise prevention and control. Convolutional neural network (CNN) can automatically extract the features of diseased tea leaves in the images. However, tea leaf imag...

Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.

Computational and mathematical methods in medicine
Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused ima...

NeuroGen: Activation optimized image synthesis for discovery neuroscience.

NeuroImage
Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individua...

RFARN: Retinal vessel segmentation based on reverse fusion attention residual network.

PloS one
Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, th...

Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI.

Radiology
Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to corre...

Extending Camera's Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising.

Sensors (Basel, Switzerland)
Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper,...

Image Enhancement Model Based on Deep Learning Applied to the Ureteroscopic Diagnosis of Ureteral Stones during Pregnancy.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the image enhancement model based on deep learning on the effect of ureteroscopy with double J tube placement and drainage on ureteral stones during pregnancy. We compare the clinical effect of ureteroscopy with double J tube pl...