• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.321-326
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• 2010

A method that can decide the existence and the severeness of flaws in ceramic materials through the use of non-destructive testing by image processing techniques, is proposed in this paper. The edges of the acquired image are first extracted using Sobel mask and the regions of the image are clustered using another mask after that. Histogram stretching is applied to each of the regions to enhance the image region-wise and objects are extracted by an edge following algorithm. Morphological information is incorporated to remove noise and detect flawed regions. The proposed method can detect flaws in the acquired images and the experimental results also supports that.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Structural Engineering and Mechanics
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• v.69 no.4
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• pp.467-477
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• 2019

Digital image correlation (DIC) is now a popular and extensively used full-field metrology technique. In general, DIC is performed by using a turnkey solution offered by various manufacturers of DIC. In this paper, a simple and economical set-up for DIC is proposed which uses easily accessible digital single-lens reflex (DSLR) camera rather than industrial couple-charged device (CCD) cameras. The paper gives a description of aspects of carrying a DIC experiment which includes experimental set-up, specimen preparation, image acquisition and analysis. The details provided here will be helpful to carry DIC experiments without specialized DIC testing rig. To validate the responses obtained from proposed DIC set-up, tension and fatigue tests on specimens made of IS 2062 Gr. E300 steel are determined. Tensile parameters for a flat specimen and stress intensity factor for an eccentrically-loaded single edge notch tension specimen are evaluated from results of DIC experiment. Results obtained from proposed DIC experiments are compared with those obtained from conventional methods and are found to be in close agreement. It is also noted that the high resolution of DSLR allows the use of proposed approach for fracture characterization which could not be carried out with a typical turnkey DIC solution employing a camera of 2MP resolution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.252-255
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• 2014

Image treatment is becoming mainstream as the demand for image restoration has drastically increased in the digital era. But in the process of acquiring, transmitting and treating video data, the salt and pepper noise damages the image. One of the major methods used for restoring images are SMF(standard median filter), CWMF(center weighted median filter) and SWMF(switching weighted median filter), but these filters all leave a bit to be desired in terms of removing noise and preserving edge. Therefore, a transformed median filter is suggested through the algorithm presented for the restoration of damaged images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.429-431
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• 2019

Noise in image processing has a direct effect on the quality of the image, and adversely affects the processing of the system including algorithms such as image segmentation, edge detection, and image recognition. Therefore, noise reduction plays an important role in the preprocessing process. In this paper, we propose an efficient algorithm to remove noise in high density of Salt and Pepper noise. The proposed algorithm removes noise by gradually expanding the filtering mask according to the density of the noise, and shows excellent noise cancellation performance even in a high density region. In order to evaluate the performance of the proposed algorithm, we compared and analyzed the existing method and the proposed algorithm through simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4866-4888
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• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.133-135
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• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.123-131
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• 2008

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of one's health like the physiological and the clinicopathological changes of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition a lot. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue region from a facial image captured and classifying tongue coating are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth are similar. The proposed method includes preprocessing, over-segmenting, detecting the edge with a local minimum over a shading area from the structure of a tongue, correcting local minima or detecting the edge with the greatest color difference, selecting one edge to correspond to a tongue shape, and smoothing edges, where preprocessing consists of down-sampling to reduce computation time, histogram equalization, and edge enhancement, which produces the region of a segmented tongue. Finally, the systematic procedure separated only a tongue region from a face image with a tongue, which was obtained from a digital tongue diagnosis system. Oriental medical doctors' evaluation for the results illustrated that the segmented region excluding a non-tongue region provides important information for the accurate diagnosis. The proposed method can be used for an objective and standardized diagnosis and for an u-Healthcare system.

• Geophysics and Geophysical Exploration
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• v.20 no.3
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• pp.176-184
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• 2017

To interpret the seismic image precisely, random noises should be suppressed and the continuity of the image should be enhanced by using the appropriate smoothing techniques. Structure-Oriented Filter-Edge Preserving (SOF-EP) technique is one of the methods, that have been actively researched and used until now, to efficiently smooth seismic data while preserving the continuity of signal. This technique is based on the principle that diffusion occurs from large amplitude to small one. In a continuous structure such as a horizontal layer, diffusion or smoothing is operated along the layer, thereby increasing the continuity of layers and eliminating random noise. In addition, diffusion or smoothing across boundaries at discontinuous structures such as faults can be avoided by employing the continuity decision factor. Accordingly, the precision of the smoothing technique can be improved. However, in the case of the structure-oriented semblance technique, which has been used to calculate the continuity factor, it takes lots of time depending on the size of the filter and data. In this study, we first implemented the SOF-EP method and confirmed its effectiveness by applying it step by step to the field data. Next, we proposed and applied the corner response method which can efficiently calculate the continuity decision factor instead of structure-oriented semblance. As a result, we could confirm that the computation time can be reduced by about 6,000 times or more by applying the corner response method.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.103-108
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• 2012

Recently, as the prevalence of high-performance mobile devices and the application of the multimedia content are expanded, Super Resolution (SR) technique which reconstructs low resolution images to high resolution images is becoming important. And in the mobile devices, the development of the SR algorithm considering the operation quantity or memory is required because of using the restricted resources. In this paper, we propose a new single frame fast SR technique suitable for mobile devices. In order to prevent color distortion, we change RGB color domain to HSV color domain and process the brightness information V (Value) considering the characteristics of human visual perception. First, the low resolution image is enlarged by the improved fast back projection considering the noise elimination. And at the same time, the reliable edge map is extracted by using the LoG (Laplacian of Gaussian) filtering. Finally, the high definition picture is reconstructed by using the edge information and the improved back projection result. The proposed technique removes effectually the unnatural artefact which is generated during the super resolution restoration, and the edge information which can be lost is amended and emphasized. The experimental results indicate that the proposed algorithm provides better performance than conventional back projection and interpolation methods.