Image Processing

                                                               Image Processing (MATLAB)

1. Satellite Image Denoising Using Bilateral Filter with SPEA2 Optimized Parameters. (Image)

     

   Abstract—Satellite imaging is being the most attractive source of information for the               governmental agencies and the commercial companies in last decade. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Many of these algorithms accept several parameters from the user to reach the best results. In the process of denoising, there is always a competition between the noise reduction and the fine preservation. If there is a competition between the objectives then an evolutionary multi objective optimization (EMO) is needed. In this work, the parameters of the image denoising algorithms have been optimized to minimize the trade-off by using improved Strength Pareto Evolutionary Algorithm (SPEA2). SPEA2 differs from the other EMO algorithms with the fitness assignment, the density estimation and the archive truncation processes. There is no single optimal solution in a multi objective problems instead there is a set of solutions called as Pareto efficient. Four objective functions, namely Mean Square Error (MSE), Entropy, Structural SIMilarity (SSIM) and Second Derivative of the image, have been used in this work. MSE is calculated by taking the square of difference between the noise free image and the deniosed image. Entropy is a measure of randomness of the content of difference image. The lower entropy is the better. The second derivate of an image can be achieved by convolving the image with the Laplacian Mask. SSIM algorithm is based on the similarities of the structures on the noise free image and the structures of the denoised image. For the image enhancement algorithms, Insight Segmentation and Registration Toolkit (ITK) is selected. ITK is an open source project and it is being developed in C++ to provide developers with a rich set of applications for image analysis. It includes tens of image filters for the registration and segmentation purposes. In this work, Bilateral Image Filter is evaluated in the field of satellite imaging for the noise removal process. The evaluated filter receives two parameters from the user side within their predefined ranges. Here, SPEA2 algorithm takes the responsibility to optimize these parameters to reach the best noise free image results. SPEA2 algorithm was implemented in Matlab and executable files of image filter were called in Matlab environment. The results of the work were represented graphically to show the effectiveness of selected method.

Keywords: Image denoising, parameter optimization, SPEA2, bilateral filter

Conclusion:

The results of the test cases showed that Bilateral Image Filter provides reasonable denoising performance if its parameters are optimized properly. Corrupted satellite images can be denosined using Bilateral Image Filter with the optimized parameters while their edges are preserved. The results also showed that SPEA2 algorithm is a very effective optimization algorithm when a multi objective optimization process is needed. In addition to Bilateral Image Filter, ITK provides several image denosing filters while preserving the features of an image. Pareto front is a powerful tool for assessment of image denoising algorithms. It can compare performances of algorithms in a wide range in the objective space. Freedom of choosing parameter configuration for denoising algorithm is also achieved from the Pareto front.

2. Image Retrieval using Interactive Genetic Algorithm (Image).

Abstract— In recent years, with the development of digital image techniques and digital albums in the Internet, the use of digital image retrieval process has increased dramatically. An image retrieval system is a computer system for browsing, searching and retrieving images from large databases of digital images. In order to increase the accuracy of image retrieval, a content-based image retrieval system(CBIR) based on interactive genetic algorithm (IGA) is proposed. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we proposed a system that splits the retrieval process into two stages. In the query stage, the feature descriptors of a query image were extracted and then used to evaluate the similarity between the query image and those images in the database. In the evolution stage, the most relevant images were retrieved by using the IGA. IGA is employed to help the users identify the images that are most satisfied to the users’ need. The experimental evaluation of the system is based on a 10000 WANG color image database. Experimental results demonstrate the feasibility of the proposed approach.

Keywords—content based image retrieval; low-level descriptors; human-machine interaction; interactive genetic algorithm.

Conclusion: 

This paper presents a new approach called image retrieval system based on IGA. Content based image retrieval is a challenging method of capturing relevant images from a large storage space. Although this area has been explored for decades, no technique has achieved the accuracy of human

visual perception in distinguishing images. Whatever, the size and content of the image database is, a human being can easily recognize images of same category. In this work, representing and retrieving the image properties of color, texture and edge are used using interactive genetic algorithm (IGA) for better approximation with user interaction. CBIR is still a developing science. As image compression, digital image processing, and image feature extraction techniques become more developed, CBIR maintains a steady pace of development in the research field. Further work considering more low-level image descriptors or high-level semantics in the proposed approach.

3. FPGA Implementation of Canny Edge Detection Algorithm

Abstract - Edge detection is the first step in many computer vision applications. Edge detection of image significantly reduces the amount of data and filters out unwanted or insignificant information and gives the significant information in an image. This information is used in image processing to detect objects in which there are some problems like false edge detection, missing of low contrast boundaries, problems due to noise etc. In this paper Canny Edge Detection algorithm is implemented. Canny Edge Detection algorithm for stored image is implemented on Virtex 5 Field Programmable Gate Array (FPGA) board. Using Xilinx platform studio and Xilinx ISE output image displayed on Video Graphics Array (VGA) monitor which is interfaced with board by using DVI connector.

Keywords: - Edge detection, Canny Edge Detection, MATLAB/ Simulink, FPGA, VGA monitor.

Conclusion: 

The implementation of Canny Edge Detection Algorithm on FPGA is described, and it is carried out successfully. The hardware and software implementation of system is found to be working properly. The MATLAB/Simulink models are used for the compatibility of implementing the image processing system on FPGA. Because it has direct functions related to the image processing and it also provides the Xilinx block sets for the FPGA implementation. The Virtex-5 ML506 evaluation board is user friendly which is used to implement this system. For this system very less hardware is required such as JTAG cable, RS232 and VGA monitor. Resources used are also very less for implementation. The result of Canny Edge Detection algorithm detects more edges whit less missing edges. It is not noise sensitive. It is useful in those digital image applications where only shape and size of image is required.

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