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Abstract. The quality of Finger Print Images in image forensics plays a vital role in the the accuracy of biometric based identification and authentication system. To suppress the salt and pepper noise in fingerprint images, B-Splines have been used for interpolation. In this paper, a two stage novel and efficient algorithm for suppression of salt and pepper impulse noise for noise levels ranging from 15 % to 95 % using B-splines interpolation is being proposed. The algorithm removes salt and pepper impulse noise from the image in the first stage and in second stage, an edge preserving algorithm has been proposed which regularizes the edges that have been deformed during noise removal process.

Abstract. The accuracy of a proper Biometric Identification and AuthenticationSystems in Image Forensics depends on the image quality to arrive at a reliable and accuracy result. To get a noise-free fingerprint image, they are applied under the pre-processing and filtering tasks. The Fingerprint Recognition system is often demanded by the accuracy factor. In this paper an attempt is made to evaluate the filtering techniques in the removal of Salt & Pepper Noise. This work proposes a faster and an efficient way to remove salt-and-pepper impulse noise and also the edge-preserving regularization of the henceforth obtained finger print noise free image. In this paper, we propose a two phase mechanism where the noisy pixels are identified and removed in the first phase and only these noisy pixels are involved in cardinal spline edge regularization process in the second phase. Promising results were found even for Noise levels as high as 90% with the proposed algorithm. The results were found to be much better than the previously proposed nonlinear filters or regularization methods both in terms of noise removal as well as edge regularization for image forensics.

In this paper, we propose a novel method which is an effective implementation of Population Particle Swarm Optimization aiming at optimizing the noise removal process in the case of grayscale images contaminated with salt and pepper noise. A new neighborhood average filter has been used in conjunction with APSO for noise removal. Simulations reveal that the proposed scheme which has been designed specifically for noise removal works well in suppressing noise impulses in images corrupted with different levels of noise. The results of the proposed algorithm are compared with those obtained by PSO-CNN method for gray-scale image noise cancellation.Keywords-Adaptive PSO, noise removal, neighbourhood average filter, salt and pepper noise, particle swarm.