An accurate discrete Fourier transform for image processing

Beaudoin, Normand; Beauchemin, Steven S.

doi:10.1109/icpr.2002.1048189

Cited by 23 publications

(10 citation statements)

References 6 publications

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“…The DFT is a common approach used to compute the frequency information as discrete. The frequency information is considered an important way in the picture enhancement [ 33 , 37 ]. Therefore, to make a beneficial way of sharpening, the frequency information of Fourier transform is combined with the second derivative masking of Laplacian filter in the novel technique.…”

Section: Proposed Methodologymentioning

confidence: 99%

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

Khan¹,

Khan

Alharbi

et al. 2021

Journal of Healthcare Engineering

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In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed on the images before applying fusion techniques. In this paper, along with the novel concept, a new sharpening technique, Laplacian filter + discrete Fourier transform (LF + DFT), is also proposed. The LF is used to recognize the meaningful discontinuities in an image. DFT recognizes that the rapid change in the image is like sudden changes in the frequencies, low-frequency to high-frequency in the images. The aim of image sharpening is to highlight the key features, identifying the minor details, and sharpen the edges while the previous methods are not so effective. To validate the effectiveness the proposed method, the fusion is performed by a couple of advanced techniques such as stationary wavelet transform (SWT) and discrete wavelet transform (DWT) with both types of images like grayscale and color image. The experiments are performed on nonmedical and medical (breast medical CT and MRI images) datasets. The experimental results demonstrate that the proposed method outperforms all evaluated qualitative and quantitative metrics. Quantitative assessment is performed by eight well-known metrics, and every metric described its own feature by which it is easily assumed that the proposed method is superior. The experimental results of the proposed technique SWT (LF + DFT) are summarized for evaluation matrices such as RMSE (5.6761), PFE (3.4378), MAE (0.4010), entropy (9.0121), SNR (26.8609), PSNR (40.1349), CC (0.9978), and ERGAS (2.2589) using clock dataset.

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Section: Proposed Methodologymentioning

confidence: 99%

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

Khan¹,

Khan

Alharbi

et al. 2021

Journal of Healthcare Engineering

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“…The Fourier Transform Method (FTM) [11,12] is divided into two main stages (Fig. 7), the image transform (green part) and Fourier transform (red part).…”

Section: Fourier Transform Methodsmentioning

confidence: 99%

Comparison of Neural Networks with Feature Extraction Methods for Depth Map Classification

Sýkora¹,

Kamencay²,

Hudec³

et al. 2020

AiMT

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In this paper, a comparison between feature extraction methods (Radon Cosine Method, Canny Contour Method, Fourier Transform, SIFT descriptor, and Hough Lines Method) and Convolutional Neural Networks (proposed CNN and pre-trained AlexNet) is presented. For the evaluation of these methods, depth maps were used. The tested data were obtained by Microsoft Kinect camera (IR depth sensor). The feature vectors were classified by the Support Vector Machine (SVM). The confusion matrix for the evaluation of experimental results was used. The row of confusion matrix represents target class of tested data and the column represents predicted class. From the experimental results, it is evident that the best results were achieved by proposed CNN (97.4%). On the other hand, the pre-trained AlexNet scored 93.7%.

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“…In the Fourier domain, each point represents a particular spatial frequency contained in the temperature distribution. The Fourier transform is used in a wide range of applications, such as image analysis, image filtering and image reconstruction [19][20][21][22][23][24][25][26][27]. Fourier transform can be used to reconstruct the temperature profile of the sheet.…”

Section: Estimation Of Sheet Temperaturementioning

confidence: 99%

A New Method for Estimation and Control of Temperature Profile over a Sheet in Thermoforming Process

Chy

Boulet

2010

2010 IEEE Industry Applications Society Annual Meeting

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This paper presents a new method for the estimation of surface temperature of a sheet using temperature sensors at different positions of the sheet. The proposed method is developed and designed in such a way that it can estimate the temperature profile over the entire sheet through its spatial harmonics and control the spatial harmonics to obtain the desired temperature profile. Computational cost is also considered during the development of the proposed method. A PI controller is used to control each spatial harmonic to achieve the desired temperature profile over the sheet. The proposed estimation method of the temperature profile is based on the twodimensional FFT. Each component of the two-dimensional space temperature harmonics is controlled by adjusting the heater temperatures. The performance of the proposed temperature estimation technique is tested in simulation for different desired temperature profile to compare with a simple method of temperature estimation based on the weighted average temperature of the sensors surrounding the location of interest. Thereafter, the performance of the proposed temperature control technique based on the spatial harmonics is compared with the conventional control technique in simulation. The superiority of the proposed method for the estimation of temperatures and hence the accuracy of the developed controller and its prospective real-time application is evidenced by the results.

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An accurate discrete Fourier transform for image processing

Cited by 23 publications

References 6 publications

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

Comparison of Neural Networks with Feature Extraction Methods for Depth Map Classification

A New Method for Estimation and Control of Temperature Profile over a Sheet in Thermoforming Process

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