A new recoloring method to improve visibility of indiscriminable colors for protanopes or deuteranopes is proposed. In the proposed method, yellow-blue components of a color image perceived by protanopes/deuteranopes are adequately modified. Moreover, the gamut mapping is considered to obtain proper output color values in this method.

This paper proposes a robust bilateral filter which can handle mixed Gaussian and impulsive noise by hybridizing the conventional bilateral filter and the switching median filter. The effectiveness of the proposed method is verified in comparison with other conventional methods by some experiments using the natural digital images.

C1 class smooth interpolation by a fuzzy reasoning for a small data set is proposed. The drafting technique of a human expert is implemented by using a set of fuzzy rules. The effectiveness of the present method is verified by computer simulations and by applications to the practical interpolation problem in a power system.

A method obtaining a monochrome image which can rebuild colors is proposed. In this method, colors in an input image are quantized under a lightness constraint and a palette, which represents relationship between quantized colors and gray-levels, is generated. Using the palette, an output monochrome image is obtained. Experiments show that the proposed method obtains good monochrome and rebuilt color images.

In this paper, impulse noise removal for digital images is handled. It is well-known that switching-type processing is effective for the impulse noise removal. In the process, noise-corrupted pixels are first detected, and then, filtering is applied to the detected pixels. This switching process prevents distorting original signals. A noise detector is of course important in the process, a filter for pixel value restoration is also important to obtain excellent results. The authors have proposed a local similarity-based filter (LSF). It utilizes local similarity in a digital image and its capability against restoration of orderly regions has shown in the previous paper. In this paper, first, further experiments are carried out and properties of the LSF are revealed. Although LSF is inferior to an existing filter when disorderly regions are processed and evaluated by the peak signal-to-noise ratio, its outputs are subjectively adequate even in the case. If noise positions are correctly detected, capability of the LSF is guaranteed. On the other hand, some errors may occur in actual noise detection. In that case, LSF sometimes fails to restoration. After properties are examined, we propose two effective extensions to the LSF. First one is for computational cost reduction and another is for color image processing. The original LSF is very time consuming, and in this paper, computational cost reduction is realized introducing a search area. Second proposal is the vector LSF (VLSF) for color images. Although color images can be processed using a filter, which is for monochrome images, to each color component, it sometimes causes color drift. Hence vector processing has been investigated so far. However, existing vector filters do not excel in preservation of orderly pattern although color drift is suppressed. Our proposed VLSF is superior both in orderly pattern preservation and color drift suppression. Effectiveness of the proposed extensions to LSF is verified through experiments.

In this letter, we propose a new tuning method of ε value, which is a parameter in the ε-filter, using a metric between signal distributions, i.e., Hellinger distance. The difference between the input and output signals is evaluated using Hellinger distance and used for the parameter tuning in the proposed method.

This paper describes a new method of state extimation for the energy stochastic system with decibel observation mechanism. The problem here is to get the decibel-valued estimate of the energy state variable through the decibel-valued noisy observation data, where it is usual that the stochastic system is physically driven on energy scale. The main attention is paid to the adjustment between the energy quantity at the physical countermeasure side and the decibel quantity at the human evaluation side. The basic principle of state estimation is based on the Bayes' theorem which can be applicable to any non-Gaussian and/or non-linear nature of the real stochastic system. Then, it is expanded into the suitable form adapted to successive decibel-valued observation. Thus, based on the mutual relation between energy and decibel statistics, any kinds of statistics connected with Lx evaluation at the human side can be estimated by using this decibel-valued noisy observation data (Lx is defined in the acoustics field as the (100-x)% point of the sound-level distribution and it is often used as the environmental noise assessment standard because man's sense of hearing is very sensitive to the end of the sound-level distribution form). Finally, the validity and the effectiveness of the proposed method have been confirmed by application to the actually obtained room acoustics data.

For a kernel-based topographic map formation, kMER (kernel-based maximum entropy learning rule) was proposed by Van Hulle, and some effective learning rules related to kMER have been proposed so far with many applications. However, no discusions have been made concerning the determination of the number of units in kMER. This letter describes a unit-pruning rule, which permits automatic contruction of an appropriate-sized map to acquire the global topographic features underlying the input data. The effectiveness and the validity of the present rule have been confirmed by some preliminary computer simulations.

Intelligent scissors is an interactive image segmentation algorithm which allows a user to select piece-wise globally optimal contour segment corresponding to a desired object boundary. However, the intelligent scissors is too sensitive to a noise and texture patterns in an image since it utilizes the gradient information concerning the pixel intensities. This paper describes a new intelligent scissors based on the concept of the separability in order to improve the object boundary extraction performance. The effectiveness of the proposed method has been confirmed by some experiments for actual images acquired by an ordinary digital camera.

In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments.

In a food image acquired by a digital camera, its intensity and saturation components are sometimes decreased depending on the illumination environment. In this case, the food image does not look delicious. In general, RGB components are transformed into hue, saturation and intensity components, and then the saturation and intensity components are enhanced so that the food image looks delicious. However, these processes are complex and involve a gamut problem. In this paper, we propose an intensity and saturation enhancement method while preserving the hue in the RGB color space for the food image. In this method, at first, the intensity components are enhanced avoiding the saturation deterioration. Then the saturation components of the regions having the hue components frequently appeared in foods are enhanced. In order to illustrate the effectiveness of the proposed method, the enhancement experiments using several food images are done.

The estimation of the point-spread function (PSF) is one of very important and indispensable tasks for the practical image restoration. Especially, for the motion blur, various PSF estimation algorithms have been developed so far. However, a majority of them becomes useless in the low blurred signal-to-noise ratio (BSNR) environment. This paper describes a new robust PSF estimation algorithm based on Hough transform concerning gradient vectors, which can accurately and robustly estimate the motion blur PSF even in low BSNR case. The effectiveness and validity of the proposed algorithm are verified by applying it to the PSF estimation and the image restoration for noisy and motion blurred images.

In this letter, a novel color removal method considering differences of colors in an input color image and achromatic color preservation is proposed. The achromatic color preservation is assigning lightness values to gray-levels concerning achromatic pixels for natural impression. The effectiveness and validity of the proposed method are verified by experiments.