In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.

This paper presents a particle swarm optimization network (PSON) to improve the search capability of PSO. In PSON, multi-PSOs are connected for the purpose of communication. A variety of network topology can be realized by varying the number of connected PSOs of each PSO. The solving performance and convergence speed can be controlled by changing the network topology. Furthermore, high parallelism is can be realized by assigning PSO to single processor. The stability condition analysis and performance of PSON are shown.

In color image processing, preservation of hue is required. Therefore, perceptual color models such as HSI and HSV have been used. Hue-Saturation-Intensity (HSI) is a public color model, and many color applications have been made based on this model. However, the transformation from the conventional HSI (C-HSI) color space to the RGB color space after processing intensity/saturation in the C-HSI color space often generates the gamut problem, because the shape of C-HSI color space is a triangular pyramid which includes the RGB color space. When the output of intensity/saturation processing result is located in the outside of the common region of RGB color space and C-HSI color space, it is necessary to move to the RGB color space. The effective way of hue and intensity preserving saturation correction algorithm is proposed. According to the proposed saturation correction algorithm, the corrected saturation value is same as the processing result in the ideal HSI color space whose gamut same as the RGB gamut.

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

In color image processing, hue-preserving is necessary for human being. In order to preserve the hue component, the perceptual color spaces such as HSI and HSV were used for the color image processing. The Hue-Saturation-Intensity (HSI) color space is important for color image processing and many color applications are commonly based on this color space. However, the gamut of conventional HSI color space is larger than that of RGB color space. Thus, the gamut problem is often occurred after the processing intensity and saturation in the HSI color space. In this paper, a new HSI color space with completely same gamut of RGB color space is developed. The gamut problem is solved by the proposed HSI color space.

In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.

Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. It can automatically define the gray-level mapping function based on the distribution of gray-level included in the image. However, since HE does not use a spatial feature included in the input image, HE fails to produce satisfactory results for broad range of low-contrast images. The differential gray-level histogram (DH), which is contained edge information of the input image, was defined and the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to HE for many kinds of images. In this paper, we propose a generalized histogram equalization (GHE) including HE and DHE. In GHE, the histogram is created using the power of the differential gray-level, which includes the spatial features of the image. In HE, the mean brightness of the enhancement image cannot be controlled. On the other hand, GHE can control the mean brightness of the enhancement image by changing the power, thus, the mean brightness of the input image can be perfectly preserved while maintaining good contrast enhancement.

In this paper, we use a modified Gaussian filter to improve enlargement accuracy of the arbitrary scale LP enlargement method, which is based on the Laplacian pyramid representation (so called "LP method"). The parameters of the proposed algorithm are extracted through a theoretical analysis and an experimental estimation. Experimental results show that the proposed modified Gaussian filter is effective for the arbitrary scale LP enlargement method.

Measurement of an individual molar provides rich information for forensic personal identification. We propose a computer-based system for extracting an individual molar from dental panoramic radiographs. A molar is obtained by extracting the region-of-interest, separating the maxilla and mandible, and extracting the boundaries between teeth. The proposed system is almost fully automatic; all that the user has to do is clicking three points on the boundary between the maxilla and the mandible.

In this paper, we propose an enlargement method for images with Gaussian noise based on the Laplacian pyramid (LP) representation. Unlike lowpass pre-processing approaches to the LP enlargement method, an embedded approach is used in this paper. Since the amplitude of Gaussian noise signals is smaller than the amplitude of image edge signals in the predicted LP stage, we adopt a modified ε-filter in the proposed LP enlargement algorithm to reduce the Gaussian noise. Experimental results show that the proposed method can obtain high accuracy denoise enlarged images.

In this paper, we propose a novel bicubic method for digital image interpolation. Since the conventional bicubic method does not consider image local features, the interpolated images obtained by the conventional bicubic method often have a blurring problem. In this paper, the proposed bicubic method adopts both the local asymmetry features and the local gradient features of an image in the interpolation processing. Experimental results show that the proposed method can obtain high accuracy interpolated images.

While emphasizing the intensity or saturation component for getting high-quality color images, keeping the hue component unchanged is important; thus, perceptual color models such as HSI and HSV have been used. Hue-Saturation-Intensity (HSI) is a public color model, and many color applications are commonly based on this model. However, the transformation from the HSI color space to the RGB color space after processing intensity/saturation in the HSI color space usually generates the gamut problem. In this study, we clear the relationship between the RGB gamut and the HSI gamut completely. According to the result, we can check whether the processing result is located inside or outside of the RGB gamut without transforming to the RGB color space. If the processing result is judged outside of the RGB gamut, we apply the effective way of hue preserving correction algorithm which is proposed in this study to the saturation component. Experimental results demonstrate that the proposed algorithm can correct the color distortion caused by the enhancement without reducing the visual effect and it is especially useful for images with rich colors and local high component values.

Preserving hue is an important issue for color image processing. In order to preserve hue, color image processing is often carried out in HSI or HSV color space which is translated from RGB color space. Transforming from RGB color space to another color space and processing in this space usually generate gamut problem. We propose image enhancement methods which conserve hue and preserve the range (gamut) of the R, G, B channels in this paper. First we show an intensity processing method while preserving hue and saturation. In this method, arbitrary gray-scale transformation functions can be applied to the intensity component. Next, a saturation processing method while preserving hue and intensity is proposed. Arbitrary gray-scale transform methods can be also applied to the saturation component. Two processing methods are completely independent. Therefore, two methods are easily combined by applying two processing methods in succession. The combination method realizes the hue-preserving color image processing with a high arbitrariness without gamut problem. Furthermore, the concrete enhancement algorithm based on the proposed processing methods is proposed. Numerical results confirm our theoretical results and show that our processing algorithm performs much better than the conventional hue-preserving methods.

Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.

Light scattering and absorption of light in water cause underwater images to be poorly contrasted, haze and dominated by a single color cast. A solution to this is to find methods to improve the quality of the image that eventually leads to better visualization. We propose an integrated approach using Adaptive Gray World (AGW) and Differential Gray-Levels Histogram Equalization for Color Images (DHECI) to remove the color cast as well as improve the contrast and colorfulness of the underwater image. The AGW is an adaptive version of the GW method where apart from computing the global mean, the local mean of each channel of an image is taken into consideration and both are weighted before combining them. It is applied to remove the color cast, thereafter the DHECI is used to improve the contrast and colorfulness of the underwater image. The results of the proposed method are compared with seven state-of-the-art methods using qualitative and quantitative measures. The experimental results showed that in most cases the proposed method produced better quantitative scores than the compared methods.

This paper presents a new implementation of fuzzy filters for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The parameters of this filter can be adjusted by learning. Finally, simulation results demonstrate the effectiveness of the proposed technique.