To create a single image with the most information possible, two photographs of same model are combined through the process of image fusion. Many image-processingapplications, including satellite imaging, remote sens...
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Driver drowsiness is identified as a major factor leading to traffic accidents and fatalities worldwide. To address this critical public concern, researchers are developing various driver-centered drowsiness detection...
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Kinship verification from facial images presents a challenging yet intriguing problem within the fields of pattern recognition and computer vision. In this study, we introduce significant advancements by applying a pr...
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A novel imageJ plugin is designed to extend the depth-of-field (DoF) by seamlessly fusing a series of multi-focus images, allowing for in-depth analysis. Moreover, it has been tested on multi-exposure image stacks, de...
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ISBN:
(纸本)9781510673151;9781510673144
A novel imageJ plugin is designed to extend the depth-of-field (DoF) by seamlessly fusing a series of multi-focus images, allowing for in-depth analysis. Moreover, it has been tested on multi-exposure image stacks, demonstrating its adeptness in preserving intricate details within both poorly and brightly illuminated regions of 3-D specimens. The significance of this capability becomes particularly apparent when dealing with images that exhibit a limited DoF and varying exposure settings under low signal-to-noise ratio conditions. The plugin's effectiveness has been thoroughly validated through the processing and analysis of numerous image stacks featuring diverse diatom and cyanobacteria species. The proposed methodology incorporates a two-scale decomposition (TSD) scheme, complemented by the refinement of weight maps using edge-preserving filtering (EPF). This dual approach ensures the preservation of fine details in the fused image while simultaneously minimizing noise. Such innovations make this plugin a valuable tool for researchers and analysts working with complex image datasets.
The integration of infrared and visible images has become increasingly relevant in the field of remote sensing technologies and imageprocessingapplications. This technique has proven beneficial in scenarios such as ...
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To address the problem of low accuracy and poor stability of bearing diagnostic models under strong background noise, a bearing fault image recognition method is proposed that reduces the randomness of the model by av...
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Coronary Atherosclerosis is recognized as the predominant cause of Chronic Heart Failure. The standard diagnostic modality for Coronary Atherosclerosis involves echocardiography (ECO);however, the accessibility of suc...
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Unmanned underwater vehicles generally rely on high quality visual data and low latency for the applications of monitoring, exploration and search. Several methods have been proposed to improve underwater visibility b...
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Excessive angular decision diffusion-weighted imaging (HARDWI) is an effective approach for visualizing tissue microstructures, which are otherwise hard to look at the usage of conventional MRI technology. To attain t...
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Confocal microscopy offers enhanced image contrast and signal-to-noise ratio compared to wide-field illumination microscopy, achieved by effectively eliminating out-of-focus background noise. In our study, we initiall...
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ISBN:
(纸本)9781510673151;9781510673144
Confocal microscopy offers enhanced image contrast and signal-to-noise ratio compared to wide-field illumination microscopy, achieved by effectively eliminating out-of-focus background noise. In our study, we initially showcase the functionality of a line-scanning confocal microscope aligned through the utilization of a Digital Light Projector (DLP) and a rolling shutter CMOS camera. In this technique, a sequence of illumination lines is projected onto a sample using a DLP and focusing objective (50X, NA=0.55). The reflected light is imaged with the camera. Line-scanning confocal imaging is accomplished by synchronizing the illumination lines with the rolling shutter of the sensor, leading to a substantial enhancement of approximately 50% in image contrast. Subsequently, this setup is employed to create a dataset comprising 500 pairs of images of paper tissue. This dataset is employed for training a Generative Adversarial Network (cGAN). Roughly 45% contrast improvement was measured in the test images for the trained network, in comparison to the ground-truth images.
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