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An Introduction to Digital Image Analysis of Superconductors
Published in David A. Cardwell, David C. Larbalestier, Aleksander I. Braginski, Handbook of Superconductivity, 2022
Charlie Sanabria, Peter J. Lee
There are many ways to increase signal and reduce image noise. The most obvious ones are inherent to the microscope itself such as electron source, image acquisition settings, beam settings, vacuum state, and overall cleanliness of the vacuum chamber and parts. However, one must keep in mind that the sample preparation can introduce noise as well. Thin native oxide surface layers (usually undetectable by LM or SEM) can increase the noise of the image by micro-charging. For this reason, a freshly polished or ion-etched sample is recommended for optimal signal. This is particularly important for the Kikuchi band diffraction images used for crystallographic orientation mapping.
Image denoising research based on lifting wavelet transform and median filter
Published in Amir Hussain, Mirjana Ivanovic, Electronics, Communications and Networks IV, 2015
In recent years, wavelet transformations have been widely used in different fields as a popular method with better time-frequency properties and multiresolution, for denoising purposes. The lifting wavelet transformation (which is also called the second generation wavelet) was introduced by Sweldens (Shark &Yu 2000, Weyrich & Warhola 1998) and serves as a new way to construct wavelets. It can make computation faster, has the capability to finish the transformation without allocating additional memory, and can realize integral transformations. Due to its simplicity and effectiveness, more attention has been given to this new technology. Image noise can be roughly divided into impulse noise (such as salt-and-pepper noise) and Gauss noise. Impulse noise can be better restrained by median filters while completely retaining the edges and other detailed features(Zhang et al. 2005, Zhang et al. 2004). The lifting wavelet transformation proposed can make computation more rapid and can also restrain white Gauss noise very well (Ren et al. 2013, Cai 2011)like the traditional wavelet transformation.
Noise reduction of retinal image for diabetic retinopathy assessment
Published in Ahmad Fadzil Mohamad Hani, Dileep Kumar, Optical Imaging for Biomedical and Clinical Applications, 2017
Ahmad Fadzil Mohamad Hani, Toufique Ahmed Soomro, Ibrahima Faye, Nidal Kamel, Norashikin Yahya
Image noise is a variation of brightness or colour information in image, and is usually an aspect of electronic noise. It can be produced by the circuitry of digital camera. In image processing, noise reduction and restoration of an image is expected to improve the qualitative inspection of an image and the performance criteria of the quantitative image analysis techniques. The main purpose of denoising the image is to restore the details of the original image as much as possible. The criteria of the noise removal problem depend on the noise type by which the image is corrupted. In the field of reducing the image noise, several types of linear and non-linear filtering techniques have been proposed.
Target detection based on a new triple activation function
Published in Systems Science & Control Engineering, 2022
Guanyu Chen, Quanyu Wang, Xiang Li, Yanyun Zhang
An ideal target detector should have both a high precision and high efficiency. Among them, the high precision mainly includes the accuracy of locating and recognition of the target, and the main problem is that different instances of the same type of target often have different colours, shapes, postures, etc., and the detection effect is largely affected by the background. For example, in pedestrian detection, a pedestrian target on a sunny day is often easier to detect than a pedestrian target in heavy fog. In addition, image noise is also an important factor affecting detection accuracy. The high efficiency mainly includes time efficiency, memory efficiency and storage efficiency in the detection process. In the target detection process, especially in the field of real-time target detection, the requirements for time efficiency are very high. For example, in the field of autonomous driving, real-time detection of various targets in front of the vehicle is required. If the frame number of the front camera is 30 FPS, the detection frame number of the target detector is required to reach the corresponding level, otherwise it will be difficult to be applied in the real world. Since a large number of targets will be encountered during the detection process, and each target needs to be identified and located, the memory and storage level of the detector should also be highly efficient.
Image Enhancement and Implementation of CLAHE Algorithm and Bilinear Interpolation
Published in Cybernetics and Systems, 2022
Venkatesh S., John De Britto C., Subhashini P., Somasundaram K.
Noise reduction is a key point of contention in image processing. Image noise is an unexpected shift in a captured image’s brightness or color contrast information. This is the deterioration of the picture signal brought on by outside factors. Images with many noise sources have the characteristic that the noise is more pronounced in areas that are brighter. However, it normally happens over time. Pre-processing is used to reduce noise and blur in order to improve image quality. The primary objective of this study is to use the CLAHE technique to enhance the human-perceived image quality. Due to poor illumination, real-time image sequences that have been captured might not be visually appealing at first. The objective of this programme is to enhance real-time image quality. This solution aims to cut down on latency without sacrificing accuracy. Binarization, denoising, and contrast enhancing all use the CLAHE method. Bilinear interpolation estimates the average value of the four closest pixels in order to replace damaged pixels with new values. By using linear interpolation, first in one direction and then the other, this is accomplished. Contrast stretching is an image enhancement technique that, as the name implies, stretches a picture’s intensity values to encompass its whole dynamic range. The transfer function is always linear and increases monotonically.
A robust method for skin cancer diagnosis based on interval analysis
Published in Automatika, 2021
Haohai Zhang, Zhijun Wang, Liping Liang, Fatima Rashid Sheykhahmad
Some of these uncertainties may be generated due to the noise, brightness intensity limiting during the discretization, etc. [25,26]. Image noise contains a random variation of colour information or brightness in images and is usually a characteristic of electronic noise. It can be generated by the image sensor and digital camera or circuitry of a scanner. As noted before, the image discretizes the reality in two different facets, quantizing and sampling. In this research, the brightness of the image has been considered as a part of sampling ambiguity and a more significant fact of uncertainty.