• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.455-464
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• 2007

Lee(2007) suggested the Point-Jacobian iteration MAP estimation(PJIMAP) for noise removal of the images that are corrupted by multiplicative speckle noise. It is to find a MAP estimation of noisy-free imagery based on a Bayesian model using the lognormal distribution for image intensity and an MRF for image texture. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. In this study, the MAP estimation is computed by the Point-Jacobian iteration using adaptive parameters. At each iteration, the parameters related to the Bayesian model are adaptively estimated using the updated information. The results of the proposed scheme were compared to them of PJIMAP with SAR simulation data generated by the Monte Carlo method. The experiments demonstrated an improvement in relaxing speckle noise and estimating noise-free intensity by using the adaptive parameters for the Ponit-Jacobian iteration.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.35-41
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• 2018

In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center's values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.112-114
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• 2014

Generally, images are corrupted by the impulse or AWGN and there are cases where both of these noises are added at once. When it comes to eliminating the noises added to the image, the previous median filter is effective in removing the impulse noise and the average filter is effective for removing AWGN. However, when the complex noises are added, it lacks the noise suppression characteristics, thus in this paper, a non-linear filter algorithm for removing the complex noises was proposed. The simulation results shows the proposed algorithm has excellent de-noising capabilities of compare existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2239-2246
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• 2011

In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.72-82
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• 1996

In this paper, newly developed algorithm for enhancing images corrupted by white gaussian noise is proposed. In the method proposed here, image is subdivided into a number of subblocks, and each block is separated into cimponents corresponding to signal and noise subspaces, respectively through the signal subspace method. A clean signal is then estimated form the signal subspace by the adaptive wiener filtering. The decomposition of noisy signal into noise and signal subspaces in is implemented by eigendecomposition of covariance matrix for noisy image, and by performing blockwise KLT (karhunen loeve transformation) using eigenvector. To reduce the perceptual noise level and distortion, wiener filtering is implementd by adaptively adjusting noise level according to activity characteristics of given block. Simulation results show the effectiveness of proposed method. In particular, edge bluring effects are reduced compared to the previous methods.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.197-205
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• 2014

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.872-878
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• 2012

It is quite important to improve the visual acuity of a medical image by suppressing noisy parts and simultaneously keeping the details of signal components to draw the accurate diagnostics. With this aim, we suggest a novel method to generate Rotational Kernel Transformation (RKT) filter mask with applying Bresenham's algorithm and implement an nonlinear filtering algorithm to eliminate noises. As a result, we can find the fact that RKT filter mask can be automatically created and the visual acuity of a corrupted image can be elevated in terms of the signal-to-noise ratio (SNR) with applying the RKT filter.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.22-28
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• 1996

In color indexed images using the palette, the corrupted indices cause serious quality degradation in the reconstructed images at a receiver. In this paper, using the human visual characteristics of color perception, we propose the color image palette minimizing the quality degradation and the reconstruction error. We define the new measure to compare the performance of palette .construction algorithms and show the effectiveness of the proposed method under the bit error condition by computer simulation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.16.2-21
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• 1998

Old movies are often corrupted by randomly located blotches and scratches. In this paper were present an efficient method for detection and removal of these distortions. The presented method is composed of two separate steps: the detection process and the restoration process. In the detection process, blotch locations are detected through global motion segmentation, the sequential approach to motion segmentation, a robust model-fit criterion and so on, we form the algorithm for the algorithm for the global motion segmentation tuned to the blotch detection problem. In the restoration process, the missing data of the detected blotch areas are temporally extrapolated from the corresponding image areas at the preceding or the succeeding image frame with considering the global motion segmentation results. We apply the presented method to moving image sequences distorted by artificial blotches. The method works very well and provides a subjective improvement of picture quality.