Image Fusion Technique Based on PCA and Fuzzy Logic Essay

Fuzzy logic is a convenient way to map an input space to an output space. Mapping input to output is the starting point for everything. Consider the following examples:

Abstract: Bad weather, such as fog, haze significantly degrade the visibility of the scene. Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. It is due to the atmosphere particles that absorb and scatter the light. Removing haze means increases the visibility of the scene and applicable for both color and gray images. The method is a fusion based strategy that derives the inputs and weight maps only from the original degraded image. To minimize the artifacts introduce the weight maps. The information of the derived inputs to preserve the regions with good visibility, by computing three measures: luminance, chromaticity, and salience

Abstract: In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting .In the field of reducing the image noise several type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image enhancement have been considered, each of which has their own limitation and advantages.

Abstract— This paper proposed two set of edge-texture features is such as Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for object recognition. The proposed DLBP and DRLTP are resultant from the disadvantage of the Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP).The LBP system and the RLBP system are mapped in the same block .The proposed feature indentifies the problem of discrimination between a bright object against dark background and vice-versa. The proposed feature also retains contrast information for object contours the LBP, LTP and RLBP discards. By the proposed features the objects is further analyzed for the accurate position of the object in the given image.

Computer Topography images are often corrupted by salt and pepper noise during image acquisition and /or transmission, reconstruction due to a number of non-idealities encountered in image sensors and communication channels. Noise is considered to be the number one limiting factor of CT image quality. A novel decision-based filter, called the wavelet based multiple thresholds switching (WMTS) filter, is used to restore images corrupted by salt-pepper impulse noise. The filter is based on a detection-estimation strategy. The salt and pepper noise detection algorithm is used before the filtering process, and therefore only the noise-corrupted pixels are replaced with the estimated central noise-free ordered mean value in the current filter window. The new impulse detector, which uses multiple thresholds with multiple neighborhood information of the signal in the filter window, is very precise, while avoiding an undue increase in

Image processing refers to the construction of an image for further analysis and use. Image taken by a camera or same techniques are not actual in a form that can be used by image analysis process. The technique involves in image enhancement need to be simplified, enhanced, filtered, altered, segmented or need improvement to reducing noise, etc. Image processing is the collection of routines and techniques that alter, improve, enhance or simplify an image. Image enhancement is one of the important parts of digital image processing where image undergo for visual inspection or for machine analysis without knowledge of its source of degradation. The processes involve to bring out specific application of an image so that the result is more suitable that the original image. Image can be enhanced in various ways such as contrast enhancement, intensity, density slicing, edge enhancement, removal of noise, and saturation transformation.[1]

Nowadays digital cameras which is used to capture images and videos are storing it directly in digital form. But this digital data ie. images or videos are corrupted by various types of noises. It may cause due to some disturbances or may be impulse noise. To suppress noise and improve the image performances we use image processing schemes. In this paper they uses Kalman filter to remove the impulse noise. The Kalman filter is digital signal processing based filter. It estimates three states past, present and future of a system.[10] To remove noise from video sequences they utilize both temporal and spatial information. In the temporal domain, by collecting neighbouring frames based on similarities of all images, to remove noise from a video tracking sequence they given a low-rank matrix recovery phenomena. [11]

Recently, we developed another efficient method for matching areas in the remote sensing images using our Contourlet-based key points with the development of a simple descriptor. A matching region was formed by the convex hull of the key points matching in both images. These regions could be used for matching, fusion, and registration of remote sensing images.

Here is a small tutorial that suffices you with the basic concepts required to put up eyes on your

Introduction:Image processing is a field that deals with manipulation of image with intent to carry out to enhance image and to extract some useful information from it. It usually deals with treating images at 2D signals and applying signal processing methods to them. It can be generally defined as a 3 step process starting by importing the image. Continuing with its analysis and ending with either an alter image or an output.

Morphological operators take a binary image and structuring element as an input and combine them using a set operator such as union and intersection. Structuring element have dimension of 3*3 and its origin is at the center pixel. The pixels of the structuring element are compared with each pixel of an image. If it matches the condition defined by the set operator then the pixel is set to a pre-defined value (0 or 1 for binary images). There are two basic types of morphological operator, which is used in this algorithm.

In this paper Gagandeep kaur and Anand Kumar Mittal have proposed a new hybrid technique and comparison of results of two image using it. They also proposed comparison of new hybrid algorithm with older. Discrete cosine transform and variance combination with hybrid discrete wavelet transform. These techniques provides good results in terms of Pseudo signal to noise ratio and mean square error. [11]

After the image has been processed using the 3 frameworks proposed the image needs to be classified which is done using the image classification technique of image mining. Classification can be carried out by applying the method of supervised classification and unsupervised classification but here we will mainly focus upon supervised classification.

The method of feature extraction is based on the spatial distribution of the black and white pixels in the image space. We are assuming that the difference of distribution of pixels for each digit are sufficient enough to classify them.

Every human iris formation starts from third month of our developing life. The iris looks like colored area visible between the pupil and white area (sclera). The human iris image is shown in Figure-1. The iris texture must be a stable pattern for every human from birth onwards. The iris texture consists of hollows, thin tubes, striae and filaments that make attractive to identify individuals for biometric.