A quality-aware Energy-scalable Gaussian Smoothing Filter for image processing applications

Garg, Bharat; Sharma, Govind

doi:10.1016/j.micpro.2016.02.012

Cited by 44 publications

(18 citation statements)

References 7 publications

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“…The larger the value of SD, the wider is the peak. However, the kernel may fail to preserve the brightness of the image even though it allows fast computation due to its separable nature (Garg & Sharma, ).…”

Section: Methodsmentioning

confidence: 99%

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Yeap

Sim

Tso

2018

Microscopy Res & Technique

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Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.

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Section: Methodsmentioning

confidence: 99%

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Yeap

Sim

Tso

2018

Microscopy Res & Technique

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“…With the Gaussian filtering method, various image/video processing applications such as edge detection, image blurring and mosaicization are easily performed [21]. The general mathematical expression of the Gaussian filter is given in Equation 3, and the mathematical expression of the Gaussian filter for two-dimensional images is given in Equation 4 [23].…”

Section: Gaussian (Smoothing / Blurring) Filtermentioning

confidence: 99%

“…where ( , ) represents gaussian filter value, and represent row and columns and is the standard deviation of the Guassian distribution. Standard deviation plays an important role in the behaviour of the Gaussian function[23].…”

mentioning

confidence: 99%

A New Hybrid Filter Approach for Image Processing

Aksoy

Salman

2020

Sakarya University Journal of Computer and Information Sciences

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Today, with the rapidly advancing technology, the importance of image processing techniques is increasing. Image processing is used in many areas from facial recognition to plant disease identification. One of the important image processing stages is the filtering stage used for smoothing images and object detection. Among these filtering techniques, basic filtering techniques such as mean, median and Gaussian are used in image processing. However, these filtering techniques are known to be insufficient to achieve the desired results in some cases. In this study, a new hybrid filtering approach named Mean-Median-Gaussian (MMG) is presented using these three basic filtering techniques. It has been demonstrated that the obtained MMG hybrid algorithm gives more successful results than these three basic filtering techniques in smoothing the images and determining the boundary lines.

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“…In some applications, the output of the digital computer (digital signal) can be transformed back to analog signal for controlling analog plant. To improve the quality of conversion, usually antialiasing filtering is applied to analog signal before it is converted to digital signal [5], and smoothing filtering is applied to digital signal before it is converted back to analog signal [6]. Figure 1 describes a generic signal conversion system where transducer is a device that converts a physical quantity from the analog plant such as pressure, temperature, velocity, light intensity, or position into an electrical signal (current, voltage, or frequency), and vice versa.…”

Section: The A/d Conversionmentioning

confidence: 99%