• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.97-105
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• 1994

Transform image coding using DCT is proved to be efficient in the absence of channel error but its performance degrades rapidly over noisy channel. In this paper, in the case of appling bit selcetive error correction coding that protects some significant bits in a codeword, an efficient allocation method of imformation bits and additive redundancy bits used for quantization and error correction coding respectively under constant transmission bit rate is proposed, and its performance is analyzed. As a result, without increasing trasmission bit rate, PSNR can be improved up to 7~8 [dB] below bit error rate $10^2$ and the image without blocking effect caused by bit error resulted from channel noise can be recostructed.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.215-218
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• 2002

The edges of an image hold much of the information in that image. The edges tell where objects are, their shape and size, and something about their texture. An edge is where the intensity of an image moves from a low value to a high value. We introduce the edge detection using the differential operator with Sobel operator and describe a nonparametric Wilcoxon test based on statistical hypothesis testing for the detection of edges. This paper proposes an efficient edge detection using Van der Waerden's statistic in original and noisy images. We use the threshold determined by specifying significance level a and an edge-height parameter. Comparison with our statistical test and Sobel operator shows that Van der Waerden method perform more effectively in both noisy and noise-free images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1677-1684
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• 1996

The theory of optimal stack filtering has been used in difference of estimates(DoE) approach to the detection of intensity edges in noisy image. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates produces the estimated edge map. In this paper, the DoE approach is modified by imposing a symmetry condition of the data used to train the two stack filers. Under this condition, the stack filters obtained are duals of each other. Only one filter must therefore be trained;the other is simply its dual. They also produce statistially unbiased estimates. This new technique is called the symmetric Difference of Estimates (SDoE) approach.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.294-296
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• 1996

The process of detecting edge in an image is an important component of many Pattern Recognition and Computer Vision applications. In many practical cases, there exist blurred images due to defocussing, movement of an object and so on. In addition, local perturbation noise can be added to the images. We propose the edge detection technique in blurred and noisy image. For this, we use Fuzzy pyramid linking mothod to remove noise and enhance the edge in images. We develop contrast intensifier using the concept of Fuzzy sets as a postprocessing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.109-112
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• 1999

Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.788-794
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.6
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• pp.75-80
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• 1983

When an image with spot noise is smoothed by smoothing filters, the noise is almost eliminated However, the image is blurred. The algorithm that reduces such an image blurring is proposed in this paper. In the algorithm, the difference between noisy image and median filtered noisy image is smoothed. As the re-smoothing method, the absolute value of the difference is median filtered and the sign of the difference is affixed on the result. It is shown that the proposed algorithm is quite effective for noise elimination and also for image blurring decrease at the same time. In this paper, the algorithm is compared with the other smoothing methods.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.113-118
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• 2015

An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.283-286
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• 2001

In this paper, we present a robust region segmentation method based on the watershed transformation of a pseudo-distance map (PDM). A usual approach for the segmentation of a gray-scale image with the watershed algorithm is to apply it to a gradient magnitude image or the Euclidean distance map (EDM) of an edge image. However, it is well known that this approach suffers from the oversegmentation of the given image due to noisy gradients or spurious edges caused by a thresholding operation. In this paper we show thor applying the watershed algorithm to the EDM, which is a regularized version of the EDM and is directly computed form the edgestrength function (ESF) of the input image, significantly reduces the oversegmentation, and the final segmentation results obtained by a simple region-merging process are more reliable and less noisy than those of the gradient-or EDM-based methods. We also propose a simple and efficient region-merging criterion considering both boundary strengths and inner intensities of regions to be merged. The robustness of our method is proven by testing it with a variety of synthetic and real images.