No abstract

Often in practice, during the process of image acquisition, the acquired image gets degraded due to various factors like noise, motion blur, mis-focus of a camera, atmospheric turbulence, etc. resulting in the image unsuitable for further analysis or processing. To improve the quality of these degraded images, a double hybrid restoration filter is proposed on the two same sets of input images and the output images are fused to get a unified filter in combination with the concept of image fusion. First image set is processed by applying deconvolution using Wiener Filter (DWF) twice and decomposing the output image using Discrete Wavelet Transform (DWT). Similarly, second image set is also processed simultaneously by applying Deconvolution using Lucy–Richardson Filter (DLR) twice followed by the above procedure. The proposed filter gives a better performance as compared to DWF and DLR filters in case of both blurry as well as noisy images. The proposed filter is compared with some standard deconvolution algorithms and also some state-of-the-art restoration filters with the help of seven image quality assessment parameters. Simulation results prove the success of the proposed algorithm and at the same time, visual and quantitative results are very impressive.

Euclidean distance is one of the oldest methods for mapping distance between two points. It is highly demandable for matching process. Recently there are many techniques for matching fingerprints. Using Euclidean distance, minutiae based fingerprint matching gives accurate matching results. Euclidean Distance is a distance matching technique which is broadly perusal in computational geometry, image processing, computer graphics and pattern recognition. According to the Euclidean distance formula, simply in the plane the distance between two points is map, and the resulting distance is match with the resulting distance of reference fingerprint for matching. Normalization is significant enhancement technique that applied to renovate the contrast in an image. In the case of noisy fingerprint images, normalization is quite important technique for better and accurate outcomes. This paper deals with to perform Euclidean distance between minutiae points for provide robustness of our algorithm for matching fingerprints to reference fingerprint. The process of determining Euclidean distance is done by a tool of Image processing i.e. Matlab.

No abstract

No abstract