The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and these disadvantages are overcome by Stationary Wavelet Transform and Dual Tree Wavelet Transform. The conventional convolution-based implementation of the discrete wavelet transform has high computational and memory requirements. Lifting Wavelets has been developed to overcome these drawbacks. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. And there are three levels of image fusion namely Pixel level, Area level and region level. This paper evaluates the performance of all levels of multi focused image fusion of using Discrete Wavelet Transform, Stationary Wavelet Transform, Lifting Wavelet Transform, Multi Wavelet Transform, Dual Tree Discrete Wavelet Transform and Dual Tree Complex Wavelet transform in terms of various performance measures.

In recent years, prediction of cancer at earlier stages is obligatory to increase the chance of survival of the afflicted. The most dreadful type is lung cancer, which is identified as one of the most common diseases among humans worldwide. In this research work, the raw input image which usually suffers from noise issues are highly enhanced using Gabor filter image processing. The region of interest from lung cancer images are extracted with Otsu’s threshold segmentation method and 5- level HAAR discrete wavelet transform method which possess maximum speed and high accuracy. The proposed Enhanced Artificial Bee Colony Optimization (EABC) is applied to detect the cancer suspected area in CT (Computed tomography) scan images. The proposed EABC implementation part, utilizes CT (Computed Tomography) scanned lung images with MATLAB software environment. This method can assist radiologists and medicinal experts to recognize the illness of syndromes at primary stages and to evade severe advance stages of cancer.

Automatic License Plate Recognition system (ALPR) is essential to implement law enforcement and traffic control on transportation systems. ALPR systems consist of the tasks: license plate localization, character segmentation, and character recognition; due to the positioning of the vehicles, the localized license plate images are mostly skewed which need to be de-skewed before proceeding to character segmentation. In this paper, a novel Skew Correction Algorithm is proposed focusing on boundary line that optimizes speed and accuracy by using the Hough transforms to get the skew corrected License plate image.

Abstract-Computer network is supported by network security, network security is facing a lot of problem in real world. The proposed authentication security system supports to avoid database hacking and spoof matching process of web-based network. The proposed security system combines the usage of both fingerprint biometrics and pin-number. The fingerprint biometrics includes the modules such as ridge enhancement, feature extraction, core detection and baseline matching. Applied biometric cryptosystem supports to save the fingerprint feature values and user mobile numbers as an encrypted format in the database. So the hackers can't steal the real feature database values. Corrupting the device through the masking techniques can be avoided using the multi security system. If hackers use masking image modal against database fingerprint feature value, it will give the matching ratio in between 80% to 90%. At that time a random pin will be generated and send to the registered mobile number, so the unauthenticated person can't access the device, at the same time the authenticated persons can login using the corresponding pin number. In the cloud network, the public cloud processes the encrypted database handling and the private cloud handles the decryption and the biometrics verification process. This multi modal authentication system surely increases the network security. This security system can be used in ATM, e-commerce website security and cloud network security.

Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content.

Automatic License Plate Recognition (ALPR)is used in Intelligent Transportation systems wherein they are deployed for automatic ticketing of vehicles at car parking area, tracking vehicles during traffic signal violations and related applications. Out of the three major modules of ALPR systems, namely 1. localization of license plate from vehicle image, 2. segmentation of the characters images from the localized license plate and 3.Recognition of segmented characters images as license plate number, localization of potential license plate from the vehicle images is the most challenging task due to the huge variations in size, shape, color, texture and spatial orientations of license plate regions in such images. LP Localization fails often due to the presence of complex background and non-uniform illumination of license plate due to varying lighting conditions. In this paper, we propose a smart, simple and efficient algorithm for Indian license Plate Localization using Top Hat Transformation, which suppresses the background of image and remove the non uniform illumination. The algorithm is tested with live ALPR field images, confirming the robustness of the proposed method against adverse imaging condition.

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