Encryption of digital color image is the process of conversion original digital color image into an encrypted one to protect the image from hacking or to prevent an authorized person to get the valuable information located in the color image. The process of color image encryption-decryption is very important issue in human activities and here in this paper we will introduce a new simple, efficient and highly secure method to be used for color image encryption-decryption. The proposed method will be tested and implemented, the efficiency parameters of the proposed method will be calculated and will be compared with other methods parameters to prove the efficiency issues of the proposed method.
Noises degrade image quality which causes information losing and unsatisfying visual effects. Salt and Pepper noise is one of the most popular noises that affect image quality. In RGB color image Salt and pepper noise changes the number of occurrences of colors combination depending on the noise ratio. Many methods have been proposed to eliminate Salt and Pepper noise from color image with minimum loss of information. In this paper we will investigate the effects of adding salt and pepper noise to RGB color image, the experimental noise ratio will be calculated and the color combination with maximum and minimum numbers of occurrence will be calculated and detected in RGB color image. In addition this paper proposed a methodology of salt and pepper noise elimination for color images using median filter providing the reconstruction of an image in order to accept result with minimum loss of information. The proposed methodology is to be implemented, tested and experimental results will be analyzed using the calculated values of RMSE and PSNR.
The quality of the RGB color image degrades from the minute it is captured to the time it is displayed to the human observer. The image is subject to many kinds of distortions during the stages that it might pass through such as storing, processing, compressing, and transmitting, thus enhancing the image quality is a major important issue. This paper will introduce the effects of salt&pepper and Gaussian noises. A comparative experimental analysis will be done and some recommendation will be approved in which filter to use to reduce the effect of the noise. General TermsColor image filtering methods KeywordsAdaptive median filter, median filter, average filter, noise, PSNR..
Abstract-Finite impulse response (FIR) digital filters are known to have many distinguishable features such as stability, linear phase characteristic at all frequencies and digital implementation as non-recursive structures. FIR filter design can be considered as an optimization problem. In this paper an estimation method of FIR filter parameters is proposed. The method relies on establishing a relationship between the signal input parameters and the filter parameters. An FIR lowpass filter was implemented and tested using various parameter values. The results showed efficient performance characteristics of FIR lowpass filters.Keywords-FIR lowpass filter, transition bandwidth, sampling frequency, window length, filter order, and stopband attenuation. I. INTRODUCTIONDesigning digital filters involves the determination of a set of filter coefficients to meet a set of design specifications. Digital filters can be classified in two categories: finite impulse response (FIR) filters and infinite impulse response (IIR) filters. By varying the weight of the coefficients and number of filter taps, virtually any frequency response characteristics can be realized with an FIR filter. FIR filter is an attractive choice because of the ease in design, linear phase shift property and stabilityDesigning an FIR digital filter require specifying passband, stopband, and transition band. In passband, frequencies are needed to be passed unattenuated. In stopband, frequencies needs to be passed attenuated. Transitionband contains frequencies which are lying between the passband and stopband. Therefore, the entire frequency range is split into one or even more passbands, stopbands, and transition bands. In practical, the magnitude is not necessary to be constant in the passband of a filter. A small amount of ripple is usually allowed in the passband. Similarly, the filter response does not to be zero in the stopband. A small, nonzero value is also tolerable in the stopband as shown in Fig 1. The transition band of the filter as shown in Fig.1 is between the passband and the stopband. The frequency ω p denotes the edge of the passband, and the band-edge frequency ωs defines the edge of the stopband. So, the difference of ω s and ω p is the width of the transition band, i.e. ω t = ω s -ω p .The ripple in the passband of the filter is denoted as δp, and the magnitude of the filter varies from 1-δp to 1+ δp. δs are the ripple in the stopband.Usually we use a Logarithmic scale to show the frequency response, hence, the ripple in the passband is 20log10δp dB, and the ripple in the stopband is 20log10δs dB. [[3], [4]] Different methods are used to design a lowpass filter such as: window method, frequency sampling method, and optimization method.In window method, a truncated ideal lowpass filter with a certain bandwidth is generated, and then we use a chosen window to get certain stopband attenuation. The filter order L can be adjusted to meet a specified roll-off rate in the transition band. Any finite-length (order) of the ideal lowpass impu...
Preserving confidentiality, integrity and authenticity of images is becoming very important. There are so many different encryption techniques to protect images from unauthorized access. Matrix multiplication can be successfully used to encrypt-decrypt digital images. In this paper we made a comparison study between two image encryption techniques based on matrix multiplication namely, segmentation and parallel methods.
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