Sanjoy Das

Development of firefly algorithm via chaotic sequence and population diversity to enhance the image contrast

Nature-inspired algorithms have been applied in the optimization field including digital image processing like image enhancement or segmentation. Firefly algorithm (FA) is one of the most powerful of them. In this paper two different implementation of FA has been taken into consideration. One of them is FA via lévy flight where step length of lévy flight has been taken from chaotic sequence. Chaotic sequence shows ergodicity property which helps in better searching. But in the second implementation chaotic sequence replaces lévy flight to enhance the capability of FA. Population of individuals has been created in every generation using the information of population diversity. As an affect FA does not converges prematurely. These two modified FA algorithms have been applied to optimize parameters of parameterized contrast stretching function. Entropy, contrast and energy of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it has been shown that FA with chaotic sequence and population diversity information outperforms the Particle swarm optimization and FA via lévy flight.

An Improved Cuckoo Search based Optimal Ranged Brightness Preserved Histogram Equalization and Contrast Stretching Method

This paper is organized into two main parts. In the first part, two methods have been discussed to preserve the original brightness of the image which are Parameterized transformation function and a novel variant of modified Histogram Equalization HE method. In this study both methods have been formulated as optimization problems to increase the efficiency of the corresponding methods within reasonable time. In the second part, a novel modified version of Cuckoo Search CS algorithm has been devised by using chaotic sequence, population diversity information etc to solve those formulated optimization problems. A new Co-occurrence matrixs features based objective function is also devised to preserve the original brightness. Peak-signal to noise ratio PSNR acts as objective function to find optimal range of enhanced images. Experimental results prove the supremacy of the proposed CS over traditional CS algorithm.

Performance Enhancement of Differential Evolution by Incorporating Lévy Flight and Chaotic Sequence for the Cases of Satellite Images

Differential Evolution DE is a simple but powerful evolutionary algorithm. Crossover Rate CR and Mutation Factor F are the most important control parameters in DE. Mutation factor controls the diversification. In traditional DE algorithm CR and F are kept constant. In this paper, the values of CR and F are modified to enhance the capability of traditional DE algorithm. In the first modified algorithm chaotic sequence is used to perform this modification. In the next modified algorithm Levy Flight with chaotic step size is used for such enhancement. In the second modified DE, population diversity has been used to build population in every generation. As a result the algorithm does not converge prematurely. Both modified algorithms have been applied to optimize parameters of the parameterized contrast stretching function. The algorithms are tested for satellite image contrast enhancement and the results are compared, which show that DE via chaotic Levy and population diversity information outperforms the traditional and chaotic DE in the image enhancement domain.

Performance Analysis of Chaotic Lévy Bat Algorithm and Chaotic Cuckoo Search Algorithm for Gray Level Image Enhancement

Dark images can be enhanced in a controlled manner with the help of nature inspired metaheuristic algorithm. In this case image enhancement has been taken as a nonlinear optimization problem. Bat algorithm (BA) and Cuckoo Search (CS) algorithm is one of the most powerful metaheuristic algorithms. In this paper these two algorithms have been modified by chaotic sequence and levy flight. In BA levy flight with chaotic step size helps to do intensification. In CS algorithm the random walk has been done via chaotic sequence. Entropy and edge information has been used as objective function. From quantitative and visual analysis it is clear that chaotic levy BA outperforms the chaotic CS algorithm.

Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method

This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching ORBPCS method and weighted thresholded histogram equalization WTHE method. The efficiency of these two methods crucially depends on the methods associated parameters. To find the optimal values of the parameters Artificial Bee Colony ABC algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio PSNR.

Combination of histogram segmentation and modification to preserve the original brightness of the images

Image enhancement by preserving the original brightness is the main challenge of the consumer electronics field. This paper concentrates on the modification of traditional histogram equalization (HE) method to increase its brightness preserving ability. In literature mainly two types of variants have been proposed based on segmentation and modification of the histogram. Both variants are able to preserve the original brightness to some extent. Actually the efficiency of these variants of HE depend on the optimal segmentation and proper modification of histogram. This study concentrates to prove that histogram segmentation or modification which one is better to preserve the image’s original brightness and how much it can be preserved by using the combination of two variants. New hybrid variants of HE method have been proposed in this paper to prove that fact. Results of the proposed methods have been analyzed visually and mathematically.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

In this study the over-enhancement problem of traditional Histogram-Equalization HE has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization ROEBHE. In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio PSNR. The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error AMBE.

Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement

Image enhancement can be formulated as an optimization problem where one parameterized transformation function is used for enhancement purpose. The proper enhancement significantly depends on two factors- fine tuning of the parameters of the corresponding parameterized transformation function and other one is the selection of a proper objective function. In this study a parameterized variant of histogram equalization (HE) has been used for enhancement purpose and to tune the parameters of that variant a modified cuckoo search (CS) with new global and local search strategies is employed. This paper also concentrates on the selection of a proper objective function to preserve the original brightness of the image. A new objective function has been developed by combining fractal dimension (FD) and quality index based on local variance (QILV). Visual analysis and experimental results prove that modified CS with search strategies outperforms the traditional and some other existing modified CS algorithms. Considering the image’s brightness preserving capability, the proposed objective function significantly outperforms other existing objective functions.

A Comparison Between Genetic Algorithm and Cuckoo Search Algorithm to Minimize the Makespan for Grid Job Scheduling

Major subjects like heterogeneity of resources, dynamic and autonomous character of Grid resources are most important challenges for Grid job scheduling. Additionally, there are issues of various strategies being maintained by the resource providers and followed by resource users for execution of their jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic approaches. This paper compares two heuristic methods: Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for job scheduling problem in order to efficiently allocating jobs to resources in a Grid system so that the makespan is minimized. Our empirical results have proved that the CSA performs better than the GA.

Feed Forward Neural Network Approach for Reversible Logic Circuit Simulation in QCA

Quantum dot Cellular Automata (QCA) is becoming a new paradigm in nanoscale computing. Artificial Neural Network model is a promising model to design and simulate QCA circuits. This study proposes a new approach to design, model and simulate small circuit as well as large circuit. Feed Forward Neural Network (FFNN) model is used to design and simulate the reversible circuit as well as conservative circuit. The simulation result of this proposed FFNN model gives better result than exhaustive simulation of QCADesigner.