Opticalspatialfiltering using nonlinear optical materials has become very popular for implantation in optical information processing  such as edge enhancement and medical image processing   . A Fourier plan of the lens contains terms including: spatial frequency, the magnitude (positive and negative) and the phase. These values capture all information regarding two dimensional images at the Fourier plane. The spatial fre- quency is the frequency across the space that can be mapped out to the different spatial frequencies to different points in the focal plane in a 4f-image system with the nonlinear material at the Fourier plane. Therefore the Fourier spectrum contains lowspatial frequencies at the center and high spatial frequency at the edge. Therefore intensity dependent nonlinear absorption can be used to filter out undesired spatial frequency bands in the Fou- rier spectrum of the image (lowspatial frequencies at the center with high intensities and lowspatial frequencies at the edges with low intensities). Spatialfiltering with nonlinear optical materials has been demonstrated by many authors, Xuan et al. used two photon absorption and Raman scattering in nonlinear material such as ace- tone and CS 2 for contrast improvement . C. S. Yelleswarapu et al.  demonstrated the use of power limit-
different methods for fingerprint ridge image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method used a unique anisotropic filter for direct gray-scale enhancement. Both methods show some improvement in the minutiae detection process in terms of time required and efficiency . A method of enhancing fingerprintimages is described, based upon nonstationary directional Fourier domain filtering by Sherlock et al. Fingerprints are first smoothed using a directional filter whose orientation is everywhere matched to the local ridge orientation. Thresholding then yields the enhanced image. Various simplifications lead to efficient implementation on general-purpose digital computers. Use of the proposed enhancement method leads to significant improvements in the speed and accuracy of the AFIS(Automated Finger print Identification System). Chikkerur et al., introduced an approach for fingerprint enhancement based on short time Fourier transform (STFT) Analysis. STFT is a well-known technique in signal processing to analyze non-stationary signals. The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local ridge frequency. Furthermore they proposed a probabilistic approach of robustly estimating these parameters . Yang et al., proposed a novel and effective two-stage enhancement scheme in both the spatial domain and the frequency domain by learning from the underlying images. To remedy the ridge areas and enhance the contrast of the local ridges, they first enhance
Over the past decade, automatic segmentation has drawn significant interest in reach. Hence, the enhanced technique of segmentation of fingerprints using two machine learning models is described in this article. We used specific filtering methods in our algorithm to assess the quality of the obtained image.The fingerprint image is then partitioned into non-overlapping blocks of a specific size. Also, the feature vector for each block is represented by its variance, mean difference, gradient consistency, ridge orientation, and energy spectrum.Also, local variance thresholding is used to distinguish between the characteristics to be calculated or deemed null. The first machine learning classifier, K-means, is taught to divide each extracted feature into two groups (front and background region).Finally, due to the K-means classification, the second one (DBSCAN clustering) is used to remove some misclassified blocks. The contour smoothing is therefore conducted to improve the fingerprint segmented images.
It is possible to correct temporal variation using mea- surements at a base station or at a neighboring observa- tory, but it is not always possible to establish a base station or locate an observatory close enough to the sur- vey area. Therefore, if a model of ionospheric and mag- netospheric currents (such as CM4) cannot be used, leveling methods are required to reduce the effect of tem- poral variation. Crossover differences (CODs) are cal- culated in most leveling methods, and a correction in temporal variation is carried out to reduce the differ- ences by assuming that the variation is a linear, poly- nomial, or sinusoidal function of time (Yarger et al. 1978; Sander and Mrazek 1982; Mittal 1984; Hsu 1995; Wessel 2010). Here, the author has developed a new leveling method, which consists of the calculation of corrections obtained by adjusting each measurement to a weighted average of its neighboring data, and a time-domain filtering calculation of these corrections. Although the CODs themselves are not utilized in this method, they can be reduced by giving the largest weights to the nearest neighboring data. In Quesnel et al. (2009), a preliminary version of this method was applied to a CM4-corrected global marine magnetic anomaly data set, and this reduced the root mean square (RMS) COD from 78.4 to 47.7 nT. However, a detailed description of the method was not given.
Abstract: The recital of the fingerprint recognition techniques trusts comprehensively on the eminence of the input fingerprintimages. Fingerprintimages are frequently of low quality because of the perspectives of the image acquisition procedure. The imperfection of crumple structures from each separable are not archetypally well defined and it is very hard to enhance the contexts of these images. In proposed scheme, enhancement is conceded out two junctures. Algorithm first improves the images in the spatial domain with a spatial crumple compensation filter and then enhances the images in the frequency domain. The limits are crumple direction and frequency of the frequency band pass filters are predictable from the original image and the first-stage enhanced image. The output of the second stage fingerprintimages are matches with templates in the public databases and produce the minutiae matching scores.
Reliably matching fingerprintimages is an extremely difficult problem, mainly due to the large variability in different impressions of the same finger (i.e., large intra- class variations). The main factors responsible for the intra-class variations are: displacement, rotation, partial overlap, non-linear distortion, variable pressure, changing skin condition, noise, and feature extraction errors.
The proposed algorithm showed high efficiency in the segmentation problem of fingerprintimages. Thanks to the use of convolutional neural networks, the region labeling algorithm and morphological processing, a low total segmentation error of 3.151% was achieved. It is worth noting that the proposed algorithm, once tuned and trained, produces efficient and accurate segmentation on all fingerprint databases we use. This is the main difference from some other algorithms, customized for specific images.
In Image Enhancement, Image is converted in the form that it can be clear to human eyes. It is used to increase the contrast in images that are significantly dark or light. Enhancement algorithms always play attention to humans‟ sensitivity to contrast. The prime aim of image enhancement is to process the image so that the result is more appropriate than the original image. Image enhancement techniques such as contrast stretching, map each grey level into another gray level by predestined transformation. Some of the areas in which IE has wide application are noted below.
Due to the unpredictable causes of smear in fingerprint scanning as well as the texture varieties in fingerprintimages, smear identification falls into a tough image texture classification problem. Among a wide variety of image processing techniques, texture analysis has been intensively used to classify, detect, or segment images based on intrinsic properties such as roughness, granulation, and regularity . Traditionally, fixed transforms like Fourier, Haar, Cosine, Sine, and co-occurrence matrix  are used in image texture analysis. In recent years, there has been a growing interest in the application of wavelet transform to a broad range of signal and image processing applications [23–25]. Arivazhagan and Ganesan  compared the texture classification performance using a combination of wavelet statistical features and co-occurrence features of wavelet transformed images with diﬀerent feature databases. Sebe and Lew  investigated the problem of texture classification by taking into account the texture model, noise distribution, and interdependence of texture features. Livens et al.  elaborated texture analysis and image classification based on discrete and continuous wavelet decompositions. For many natural signals, the wavelet transform proves to be a more e ﬀ ective tool than the Fourier transform, especially in terms of representing specific texture features unique to di ﬀ erent tissues, because the wavelet transform provides a multiresolution representation using a set of analyzing func- tions that are dilations and translations of a few wavelets . Every transform algorithm and selection of filter banks or texture features proposed by the above papers is optimal for the specific class of inputs studied in its respective field. However, in this case of fingerprint smear identification, not a single fixed transform or filtering-based technique provides satisfactory solution for the range of possible inputs generated by variable defective fingerprint tissues. The accurate diﬀerentiation of fingerprint smear from normal tissue is diﬃcult due to the small interclass variability and the large intraclass variability in fingerprint patterns. Moreover,
3) Feature Extraction Proces: Feature extraction process depends on the above processes and it is the main part of the overall system in which it extracts the required characteristic of the fingerprint pattern. This process is very sensitive process and concentrated on illuminate the required characteristics of the Minutiae’s; this can be implemented via Minutiae detection and Minutiae enhancement and Minutia extraction.
For the Image Preprocessing of Fingerprint stage, I use Histogram Equalization and Fourier Transform to do image enhancement . And then the fingerprint image is binarized using the locally adaptive threshold method . The image segmentation task is fulfilled by a three-step approach: block direction estimation, segmentation by direction intensity  and Region of Interest extraction by Morphological operations. Most methods used in the preprocessing stage are developed by other researchers but they form a brand new combination in my project through trial and error.
There are several techniques to enhancefingerprint image in pre- processing stage but only Histogram Equalization provides much better results than other as shown in Fig: , the obvious reason behind this is the strategy typically builds the overall divergence of pictures, particularly when the usable information of the picture is depict by close divergence values. Through this modification, the intensities can be better appropriated on the histogram which permits the territories of lower neighbourhood divergence to increase a higher divergence.
Experiments using a certain type of three-axis optical tracking servo stabilized platform, its azimuth axis, pitch axis and roll axis have adopted the type KT-11 SDOF (Single Degree Of Freedom) Liquid Floated Gyro, this paper takes azimuth axis as an example, in accordance with the requirements of the Monte Carlo experiment, the same experiments were carried out several tests on the remaining axes, the results are consistent with one in this paper. In order to verify the effects of the proposed multi-wavelet filtering algorithm, we process the same sample data of gyro output signal using four different filtering methods.
Wide varieties of filtering and segmentation techniques already proposed. Different assumptions about the type of the analyzed images direct to the use of different algorithms. [Bankman, 2009] [Lucas and Sinha,1996].Anisotropic diffusion, commonly called Perona–Malik diffusion, proposed the technique in which image noise reduces and preserves edges. [1,2].In bio-medical images it is very important to preserve the complete image contents with edges[2,3]. The space-variant filter is in isotropic in nature that relies on the image value that it generates and approximates an impulse function that are closed to edges and other characteristics that should be conserved in the image. Anisotropic diffusion concept is a commonly used to eliminate noise from mammogram images and preserving edges by selecting a proper constant diffusion coefficient. The anisotropic diffusion equations reduce to the heat equation that is equivalent to Gaussian blurring [1, 2, and 3]. This considered the best for noise filtering. When the diffusion coefficient selected as an edge level, such as in Perona- Malik , the resultant equations gives smoothing within regions and forbid it through tough edges. Most of the segmentation algorithms in image processing applications based on basic properties of gray- level values. Two types of discrimination of gray level images mentioned. One is discontinuity and other is similarity. In discontinuity, image partitioning based on the sudden changes in intensity values of the pixels whereas similarity based on by selecting predefined criterion for image partitioning. The two broad categories of image segmentation based on the above properties are (1) edge-based segmentation techniques that work on edges between the regions and (2) region segmentation method work on determining regions. [Rosenfeld and Kak, 1982] [Sonka,1999]. The strategies for edge-based segmentation algorithms aim is to find out object boundaries [Sonka, 1999][Weska,1978].Thresholding is another region segmentation method; In this technique threshold values is selected to convert a gray-scale image into a binary image. Different mostly used methods are maximum entropy method, method, histogram etc. Some newly introduced methods suggest the use of fuzzy rule-based non-linear thresholds technique, which works on multi-dimensional. In this method, decision made to a segment to consider each
Abstract. In order to extract better vein feathers, image preprocessing is necessary. So this paper propose a new approach to enhanceimages. The enhancement algorithm uses guided filter (GF) to process hand vein images. The guided filter is used as an edge-preserving smoothing operator. The guided filter enhancement algorithm is effective comparing with bilateral filter (BF), histogram equalization (HE), adaptive histogram equalization algorithm (AHE) and contrast limited adaptive histogram equalization (CLAHE). We use several methods to enhance dorsal hand vein images, the recognition rate with guided filter is the best. For the security, a fake vein detection algorithm is used to discriminate the real vein and fake vein images.
Image enhancement operations consisting of a sets of techniques that trying to enhance the visible appearance of an image or to convert the image to a form more appropriate for analysis by a machine or human. The enhancement expresses accentuation or sharpening of image features, such as contrast, edges, etc . The useful information that can be derived by researchers in digital image processing by the case of image capture in a cloudy atmosphere or opaque site, this causes a problem known as lowcontrast. Which presents a bad distribution of the lighting situation in the vicinity of the image details. Lowcontrast is an important problem facing researchers working on gray images, or medical radiography images dark , or images of old documents. The main aim of this paper is the contrast enhancement gray images that have lowcontrast using FDCT-USFFT to make the images look brighter.
In literature, there are many studies available, which mainly focuses on fingerprint image segmentation. Researchers, Mehtre, B. M., & Chatterjee, B. (1989) classified the image into blocks, which is administrative specific and the size was 16 × 16 pixels. Based on the gradient distribution, each block was classified. This method is best suited for simple fingerprintimages which contain only background and foreground. Later Researchers Mehtre and Chatterjee (1989)  extended this work by leaving the grayscale variance, which will usually be lower than some threshold value. Researchers Ratha, N. K., Chen, S., & Jain, A. K. (1995)  proposed 16 × 16 blocks of classes and each one was developed based on the gray scale variance in the direction opposite to the orientation of ridges.
Low brightness and lowcontrastimages are frequently obtain in image acquisition and they are necessary to process for definite applications. OBLCAE algorithm was studied in this research paper for low illumination image enhancement in HSV color space.