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Morphological filtering is a useful technique for processing and analyzing binary and grayscale images. These filters are effective in impulsive noise filtering, feature extracting, and images enhancing. So it is important to find out method for applying morphological technique on color image and keep the colors unchanged at the same time. This paper deals with the use of morphological filters for suppression "salt & pepper" noise in color images. We have used "component-wise approach", in which grayscale morphological operations are applied to each of the three colors image components independently. We present experimental results of applying this approach for the application of noise suppression in color images.

In an electronic color-imaging device, such as a digital camera, using a single CCD (Charge Coupled Device) sensor, the color information is usually acquired in sub-sampled patterns of Red (R), Green (G) and Blue (B) pixels. The methodology to recover or interpolate the missing color components is known as color demosaicing, a type of image interpolation. This paper introduces and compares some of the most popularly demosaicing algorithms of adaptive type applied on true color images. Another comparison is done between these methods and the non-adaptive traditional methods. Also, a new method is invented, which depends on both types. The used images are of Bitmap type. Signal to noise ratio measure is used as an objective quality metric. The results of comparison are presented with some sampled images. Programming has been applied using Visual C++ 6.0 programming language.

This paper proposes a new algorithm for recognizing text object images by using fractal geometry. The fractal dimension was used as a main feature for recognizing text objects within images. Box-counting method was used to estimate the fractal dimension for image contents. In order to determine a threshold value for the textual objects within image, the fractal dimension was computed for a number of gray scale textual images. The fractal number of each pixel was calculated, then the mean value of all these fractal values were computed. The threshold value was used in recognizing and retrieving the textual objects within image. This algorithm was applied on 75 image samples, 25 image samples were used in training phase, the threshold value was determined throughout this phase; whereas 50 image samples were used in testing the algorithm. The proposed algorithm has performed extremely well with recognition rates 91.5% which is considered good performance. It is a promising technique for optical character recognition system.