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This paper proposes a hybrid system for secretly embedding images into the dithered multilevelimage. Confident hybridizations between steganography and quantum encryptions are either rare inliterature or suffer a poor effectiveness in secure communication. This paper scrambles and divides thesecret image into groups to be embedded in the blocks of the cover image using three chaos algorithms.These are Lorenz map, Henon map, and Logistic map algorithms. The encryption of embedded imagesconducted using the quantum one-time pad. Results showed that the proposed hybrid system succeeded inembedding and combining images with quantum cryptography algorithms.

Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.

In this paper, a mixed transform method is proposed based on a combination of wavelet transform (WT) and multiwavelet transform (MWT) in order to denoise medical images. The proposed method consists of WT and MWT in cascade form to enhance the denoising performance of image processing. Practically, the first step is to add a noise to Magnetic Resonance Image (MRI) or Computed Tomography (CT) images for the sake of testing. The noisy image is processed by WT to achieve four sub-bands and each sub-band is treated individually using MWT before the soft/hard denoising stage. Simulation results show that a high peak signal to noise ratio (PSNR) is improved significantly and the characteristic features are well preserved by employing mixed transform of WT and MWT due to their capability of separating noise signals from image signals. Moreover, the corresponding mean square error (MSE) is decreased accordingly compared to other available methods.

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.