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.

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.

This paper describes a color correction method of low-cost still/video camera images. Instead of using complex and non-linear equations, the concept of a three-dimensional reduced resolution look-up table is used for the real-time color gamut expansion of low-cost cameras. The proposed method analyzes the color gamut of low cost cameras and constructs 3-dimensional rule tables during the off-line stage. And, real-time color correction is conducted using that rule table. The experimental result shows that output images have more vivid and natural colors compared with originals. The proposed method can be easily implemented with small software and/or hardware resources.

A novel effective method for detection and removal impulse noise in highly corrupted color images is proposed. This detection-estimation method consists of two steps. Outliers are first detected using spatial relations between the color components. Then the detected noise pixels are replaced with the output of the vector median filter over a local spatially connected area excluding the outliers. Simulation results in a test color image show a superior performance of the proposed filtering algorithm comparing to the conventional vector median filter. The comparisons are made using a mean square error and a mean absolute error criteria.

A new adaptive multichannel filtering approach is introduced and analyzed in this paper. The technique is simpler and more appropriate than traditional approaches that have been addressed by means of groupwise vector ordering information. These filters are a two-stage filters based on rational functions (RF) using fuzzy transformations of the Euclidean and angular distances among the different vectors to adapt to local data in the color image. The output is the result of vector rational operation taking into account three fuzzy sub-function outputs. Simulation studies indicate that the filters are computationally attractive and have excellent performance such as edge and details preservation and accurate chromaticity estimation.