Infrared thermography-based diagnostics on power equipment: State-of-the-art

Many approaches to image restoration are aimed at removing either gauss or impulse noise. This is because both types of degradation processes are distinct in nature, and hence they are easier to manage when considered separately. Nevertheless, it is possible to find them operating on the same image, which produces a hard damage. This happens when an image, already contaminated by Gaussian noise in the image acquisition procedure, undergoes impulsive corruption during its digital transmission. Here we proposed an algorithm first judge the type of the noise according to the difference values of pixel’s neighborhood region and impulse noise’s characteristic. Then removes the gauss noise by modified weighted mean filter and removes the impulse noise by modified nonlinear filter. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms. The proposed method can not only remove mixed noise effectively, but also preserve image details.

http://www.ndsl.kr/soc_img/society/kimics/E1ICAW/2011/v9n3/E1ICAW_2011_v9n3_336.pdf

Restoration of Images Contaminated by Mixed Gaussian and Impulse Noise using a Complex Method

Non-destructive testing (NDT) plays a crucial role in large scale industrial production such as in the nuclear industry and bridge structures where even a small crack can lead to severe accidents. The pulsed eddy current infrared thermography testing method, as a classic non-destructive testing technology, is proposed to detect cracks in the presence of excitation sources that cause temperature changes in the vicinity of defects, which is higher than normal area. However, in the vicinity of the excitation sources, the temperature is higher than normal even if there is no defect. Traditional infrared image enhancing algorithms do not work efficiently when processing infrared images because the colors in the images represent the temperature. To address this, a novel algorithm is proposed in this paper. A weighted estimation algorithm is proposed because each pixel value has a strong relationship with its neighboring pixels. The value of each pixel is determined by calculating the values of its neighboring pixels with a specific step-size and the correlation coefficients between them. These coefficients are obtained by calculating the differences between the pixels. The experimental results indicated that the outline of the welding defect became significantly clearer after being processed using the proposed algorithm, which can eliminate the errors caused by the excitation source.

A Weighted Estimation Algorithm for Enhancing Pulsed Eddy Current Infrared Image in Ecpt Non-Destructive Testing

Nowadays, the high quality of image is required with the demand for digital image processing devices is rapidly increasing. But image always damaged by many kinds of noises and it is necessary to remove noise and the denoising becomes one of the most important fields. In many cases image is corrupted by AWGN(additive white Gaussian noise). In this paper, we proposed an improved denoising algorithm with edge preservation. The proposed algorithm averages values processed by spatial weighted filter and self adaptive weighted filter. Then we add the value which is computed by the equation considering variance of mask and the estimated noise variance. Through the experience, the proposed filter performs well on noise suppression and edge preservation properties and improves the image visual quality.

/pdf/a-study-on-improved-denoising-algorithm-for-edge-66vwewzjm1.pdf

A Study on Improved Denoising Algorithm for Edge Preservation in AWGN Environments

Recently, the expectation of quality about images over the increasing demand of digital devices is increasing with the development of the technology of the digital. But the images are degraded by a variety of causes, and the main reason is the noises. Therefore, the necessity of denoising comes to the fore, and the research for denoising is progressing dynamically. The images are mainly degraded by AWGN(additive white Gaussian noise), and the characteristics of denoising of existing methods such as mean filter are insufficient. In this paper, an algorithm combined by the spatial weighted filter and the modified adaptive weighted filter is proposed in order to effectively remove the AWGN. In the simulation result, the proposed algorithm showed excellent denoising capabilities.

/pdf/an-improved-weighted-filter-for-awgn-removal-5fsi9yg1ge.pdf

An Improved Weighted Filter for AWGN Removal

Aimed at the infrared images with mixed noise, this paper proposes a new adaptive weighted mixed filtering algorithm. The new algorithm first judges the type of the noise according to the difference values of pixel's neighborhood region. Then removes the gauss noise by self-adaptive weighted mean filter and removes the impulse noise by self-adaptive weighted median filter. The result of computer simulation on infrared images indicates that the new algorithm is superior to traditional mean filtering algorithm and traditional median filtering algorithm. The new algorithm can not only remove mixed noise effectively, but also reserve image details.

Jiahui Wang

Papers

A New Self-Adaptive Weighted Filter for Removing Noise in Infrared Images