Geeta Hanji

A new threshold-based median filtering technique for salt and pepper noise removal

Removing Noise from the image is a challenging problem for the researchers. This paper proposes a two phase threshold based median filtering technique for salt and pepper impulse noise removal. It is implemented as a two pass algorithm: In the first pass corrupted pixels are perfectly detected using min-max strategy and an adaptive working window based on estimated noise density. Second phase is a threshold based filtering technique to correct the corrupted pixels by a valid median. Experimental results have shown that the proposed technique performs far more superior than many of the efficient median based filtering techniques reported in the literature in terms of Peak Signal (PSNR) and visual perception of the images corrupted by impulse noise even to the tune of seventy percent.

A new impulse noise detection and filtering algorithm

A new impulse noise detection and filtering algorithm A new impulse detection and filtering algorithm is proposed for restoration of images that are highly corrupted by impulse noise. It is based on the average absolute value of four convolutions obtained by one-dimensional Laplacian operators. The proposed algorithm can effectively remove the impulse noise with a wide range of noise density and produce better results in terms of the qualitative and quantitative measures of the images even at noise density as high as 90%. Extensive simulations show that the proposed algorithm provides better performance than many of the existing switching median filters in terms of noise suppression and detail preservation.

An Improved Nonlinear Decision Based Algorithm for Removal of Blotches and Impulses in Grayscale Images

A nonlinear decision based algorithm for the removal of blotches in the presence of impulse noise in grayscale images is proposed in this paper. The algorithm is implemented in two stages. In the first stage, decision rule based on the switching threshold is applied to the whole image unconditionally to detect the pixels as corrupted/uncorrupted. In the second stage the new pixel value is estimated only for the corrupted pixels. The algorithm uses an adaptive length window whose maximum size is 5×5 to avoid blurring due to large window sizes. However, the restricted window size renders median operation less effective whenever noise is excessive in which case the proposed algorithm automatically switches to mean filtering. The proposed algorithm is tested on different images. The performance of the algorithm is analyzed quantitatively in terms of Mean Square Error [MSE], Peak-Signal to-Noise Ratio [PSNR], Image Enhancement Factor [IEF] and computation time and compared with other algorithms. Extensive simulations show that proposed algorithm removes the noise effectively even at noise level as high as 50% and preserves the edges without any loss, thus producing better results in terms of the qualitative and quantitative measures of the image.

Study of Existing Indian Voting System and Implementation of Hybrid Design using Biometric Security in Voting Authentication Process

Voting is an integral part of a democratic society. It is a decision making mechanism and security plays an important role in voting. In order to ensure high security, voting machine should be designed and developed with great care. According to Election authorities of India, paperless electronic voting systems are suffering from much vulnerability. By accessing the machines Election insiders and fraudsters are altering the election results. There is a need of voting system which is robust and secure. Here, an idea is proposed to upgrade the present voting system that is based on biometric traits (Iris, Fingerprint) of voter which are saved in a government database as Aadhar (U-id) number database. But one cannot have access to Aadhar (U-id) number Data base since it is a govt. Stored data base. So, a virtual data base is created here which is called as RFID number data base. This RFID number data base resembles the Aadhar (U-id) number data base. This data base includes the biometric traits of Voters. These biometrics traits provide secure and feasible authentication to the voters, thus preventing the fraud and illegal voting. General Terms Finger Print Recognition, Iris Recognition, Security, and Authentication.

A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images

A novel mean-median filter is proposed for the suppression of impulse noise and various artifacts from the digital images. Leading Diagonal Sorting Algorithm is used with the fixed 3x3 size working window to compute the median. Truncated mean is computed by defining the boundaries and truncating the pixel values in the filtering window that fall outside the defined boundary. Noise detection is carried in two steps: In the first step the ‘reference pixel’ is tested for the presence of impulses with Min-Max detection strategy. In the second stage of detection, edge preserving unique criteria is employed to further classify the pixel under test as ‘noisy’ or ‘edge belonged’. This intelligent edge preserving decision criterion decides whether the test pixel deserves the restoration or not and facilitates the restoration of the noisy pixel either by the ‘truncated mean’ or by the ‘window median’ based on the decision threshold. Performance of the proposed filter algorithm is studied on a large number of images with varying amounts of salt and pepper noise and several types of artifacts and their combinations. Simulation results prove that the proposed algorithm (PA) effectively suppresses the high density salt and pepper noise (SPN) and in addition, it performs excellent in suppressing image artifacts such as strip lines (both white and black), drop lines (both white and black), missing bands and noise blotches. The novelty of the proposed mean-median filter lies in the fact that it attempts to simultaneously suppress impulsive noise and artifacts with good edge preservation (as the maximum size of the filtering window is restricted to ‘3×3’ only) and also because a two stage detection mechanism is employed. The goodness of the proposed algorithm lies in replacing several independent filter schemes required for suppression of noise and artifacts of several types without much distorting the vital features of the image under test. The performance evaluation of the proposed algorithm is done in terms visual appearance and quantitative measures such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Enhancement Factor (IEF) and the Computation Time (CT). Simulation results are compared with other state of art algorithms to derive the meaningful conclusions. General Terms Image Processing, Noise and Artifacts Suppression.