A noise removal (de-noising) is one of the important problems in image processing applications. The noise added to the original image by changes the intensity of some pixels while other remain unchanged. Salt-andpepper noise is one of the impulse noises, to remove it a simplest way used by windowing the noisy image with a conventional median filter. Median filters are the most popular filters extensively applied to eliminate salt-andpepper noise. This paper evaluates the performance of median filter based on the effective median per window by using different window sizes. The experimental results show that median filter has a good performance in low noise densities and also in high noise densities when using high level of window sizes, but with higher window size a degree of blurring effect will be added to filtered noise.The approach used is a windowing operator technique to cut the pixels of an image, and apply filtering processing to them that take different window sizes 3*3 and 5*5 and 7*7. The results obtain for image size of 250*400.

Adaptive algorithms play a major role in all adaptive processing systems. One of the most important and wellknown algorithms is the least mean square (LMS) algorithm due to simplicity, stability and fast convergence rate. In this paper, two different types of the LMS algorithm were reviewed and analyzed: adapted and unadapted variable step size. The adapted step size varies its value according to signal features while unadapted step size varies its value with a fixed predefined constant. The performance of each algorithm measured in the scope of adaptive noise canceling system. Performance quality was determined with respect to mean square error (MSE), convergence rate and algorithm stability. According to the obtained simulation results the Adapted variable step size VSS-LMS algorithm was performed very well and converged rapidly also it was stable through the adaptation process, as a result, it behaves better than unadapted VSS-LMS algorithm. And both adapted and unadapted VSS-LMS algorithms give an enhanced performance for the ordinary fixed step size LMS algorithm.

Mean filtering algorithms are widely used for image processing especially with image smoothing or denoising systems but its hardware implementation has many restrictions. Filtering Process needs image segmentation that was done by pipelined windowing technique to scan the processed image horizontally and vertically and the size of this window determines the length of mean filter. All pixels within the specified window are drawn simultaneously and processed together in each cycle. This process produces repeated calculations that leads to depleting more hardware which can be reduced highly if an efficient implementation method is used. Another important problem with traditional implementation method of this filter is the need of more I/O pins, since all pixel values of the processed window must be read at the same cycle. These I/O pins act as a big limitations that restrict the huge data manipulation systems when implemented with any high speed hardware platform. In this paper, two novel techniques were proposed to design and implement mean filtering algorithms, that ensure simplicity of the required hardware and efficiently improve processing time as well as the needed input pins are minimized highly by reading only the new pixels of the processed window, while keeping the remaining big part as it is without any change, since it is already prepared in previous cycle. The proposed techniques depend mainly on removing the repeated computations and cancellation of division operations which result in improving the processing efficiency and highly reduce the execution time to match real time requirements. Pipelining technique is also adopted here, with the second proposed technique, to activate parallel processing scheme. So, systolic architecture were used for further reduction in computation operations and to improve overall system execution time. The obtained results proved good enhancements in processing time especially for huge image size. The efficiency of implemented design was significantly improved for large filter length, that makes the proposed techniques very useful when dealing with huge data systems. The effect of Gaussian noise was tested for gray and colored images to compare the degradation of both of them with this type of noise.

The FPGAs will continue to be used for many of today's challenging signal processing application. The reasons for this are firstly FPGAs are a form of highly configurable hardware, since it have reconfigurable gate structure which consumes more power. The second reason is MIPS (Millions of instructions executed per second) and MMACS (Millions of Multiply-AccumulateOperations per Second) requirements of an application.The main obstacle of using FPGA with processing of huge data is the limited number of pins of the FPGA kit that restrict the number of samples that must be processed simultaneously. This paper describes an efficient FPGA based hardware design with the assistance of system generator applied for image processing and filtering algorithms. The used approach is a windowing operator technique to cut specified number of pixels from an image, and apply the filtering process to them. The median filter is used here, with two window sizes 3*3 and 5*5.Xilinx software ISE 14.6 with (VHDL) language Spartan3-700A and MATLAB R2012a are the combined S/W for our application.