An Efficient Fingerprint Enhancement Technique Using Wave Atom Transform and MCS Algorithm

AbstractFingerprints are widely and successfully used for personal identification. This is mainly due to their individuality, stability through life, uniqueness among people, public acceptance and their minimum risk of intrusion. Fingerprint technology is a biometric technique utilized to identify persons based on their physical traits. The physical patterns of this technique consist of ridges and valleys that exist on the surface of fingertips. Fingerprint images are direction-oriented patterns formed by ridges and valleys. The eminence of the fingerprint image is determined by the sturdiness of a fingerprint authentication system. In order to improve the limitations of existing fingerprint image enhancement methods an efficient technique is proposed to deal with low quality fingerprint images. The proposed methodology can be divided into three modules. In the first module, the fingerprint image is subjected to denoising process where Wave atom transform is utilized. After the completion of this process the image enhancement is performed with the help of optimization algorithm. In our enhancement approach, a Modified Cuckoo Search (MCS) algorithm is used as an optimizer. This helps to look for the best gray level distribution that maximizes the objective function

Wavelet and FFT Based Image Denoising Using Non-linear Filters

We propose a stationary and discrete wavelet based image denoising scheme and an FFTbased image denoising scheme to remove Gaussian noise. In the first approach, high subbands are added with each other and then soft thresholding is performed. The sum of low subbands is filtered with either piecewise linear (PWL) or Lagrange or spline interpolated PWL filter. In the second approach, FFT is employed on the noisy image and then low frequency and high frequency coefficients are separated with a specified cutoff frequency.Then the inverse of low frequency components is filtered with one of the PWL filters and the inverse of high frequency components is filtered with soft thresholding. The experimental results are compared with Liu and Liu's tensor-based diffusion model (TDM) approach