Veysel Aslantas

Fusion of multi-focus images using differential evolution algorithm

In an image captured by a CCD/CMOS sensor with an optical lens, only the objects within the depth of field are sharply focused. One way to get an everywhere-in-focus image is to fuse the images of the same scene which are taken with different focal settings. In this paper, a novel optimal method for multi-focus image fusion using differential evolution algorithm is presented. The source images are first decomposed into blocks. Then, the sharper blocks are selected by employing a sharpness criterion function. The selected blocks are finally combined to construct the fused image. The motivation of the proposed method lies in the fact that an optimized block size could be more effective than a fixed block size. Experimental results show that the proposed method outperforms other traditional methods and genetic algorithm based method in terms of both quantitative and visual evaluations.

DWT-SVD based image watermarking using Particle Swarm Optimizer

Several watermarking schemes have been proposed with the purpose of copyright protection and access control for multimedia objects. In this paper, an optimal discrete wavelet transform-singular value decomposition (DWT-SVD) based image watermarking scheme using particle swarm optimizer (PSO) is presented. Proposed DWT-SVD based watermarking algorithm initially decomposes the host image into subbands, afterwards the singular values of each subband of the host image are modified by different scaling factors to embed the watermark image. Modifications are optimized using PSO to obtain the highest possible robustness without losing the transparency. Experimental results show improvement both in transparency and robustness under certain attacks.

Differential Evolution Algorithm For Segmentation Of Wound Images

Color-based region segmentation of skin lesions is one of the key steps for correctly collecting statistics that can help clinicians in their diagnosis. This study describes the use of differential evolution algorithm for segmentation of wounds on the skin. The abilities of differential evolution optimization algorithm, such as easiness, simple operations using, effectiveness and converging to global optimum reflected to wound image segmentation by using differential evolution algorithm in image segmentation. The system does not have the disadvantages of classical systems such as K-means clustering algorithm and the results obtained from different wound images have been discussed.

A novel fragile watermarking based on Particle Swarm Optimization

In this paper, a novel fragile watermarking scheme based on discrete cosine transform (DCT) using particle swarm optimization (PSO) algorithm is presented. Embedding watermarks in frequency domain can usually be achieved by modifying the least significant bits (LSBs) of the transformation coefficients. After embedding process is completed, a number of rounding errors appear due to conversion of real numbers into integers in the process of transformation of image from frequency domain to spatial domain. A population based stochastic optimization technique (PSO) is proposed to correct these rounding errors. Simulation results show the feasibility of employing PSO for watermarking and the accuracy of this novel method.

A novel clonal selection algorithm based fragile watermarking method

In this paper, a novel fragile watermarking method based on clonal selection algorithm (CSA), CLONALG, is presented. In Discrete Cosine Transform (DCT) based fragile watermarking techniques, there occurs some degree of rounding errors because of the conversion of real numbers into integers in the process of transformation of image from frequency domain to spatial domain. In this paper, the rounding errors caused by this transformation process are corrected by using CLONALG. Simulation results show that extracted watermark is obtained exactly the same as embedded watermark and optimum watermarked image transparency is achieved. In addition, the performance comparison of CLONALG and genetic algorithm (GA) based methods is realized.

New optimised region-based multi-scale image fusion method for thermal and visible images

On constructing a fused image by employing only individual pixels or a set of pixels within a small neighbourhood of the images (SIs) acquired from the same scene, pixel-based fusion techniques suffer from some drawbacks, such as blurring effects, high sensitivity to noise and misregistration. To overcome these drawbacks, this study proposes a new region-based image fusion method for thermal and visible images. Since different regions with certain properties need to be emphasised differently in the fused image, the corresponding regions of the SIs are optimally merged to obtain the fused image by employing multiple weighting factors (WFs). To improve the quality of the fused images, WFs were optimised by employing the differential evolution algorithm. Furthermore, a new quality metric was also developed to measure the quality of the fused images during the optimisation process. Experimental results show the feasibility of the proposed method.

An SVD based digital image watermarking using genetic algorithm

In this paper, a novel optimal watermarking scheme based on singular value decomposition (SVD) using genetic algorithm (GA) is presented. The singular values (SV) of the host image are modified to embed the watermark image. Modifications are optimised using GA to obtain the highest possible robustness without losing the transparency. Experimental results show both the significant improvement in transparency and the robustness under attacks.

A new SVD based fragile image watermarking by using genetic algorithm

In this paper, a novel fragile image watermarking scheme based on singular value decomposition (SVD) using genetic algorithm (GA) is proposed. Every line of watermark is scaled by using multiple scaling factors (SFs). Host image is divided into blocks. Watermarked image is obtained by embedding a different line of the watermark to singular values (SVs) of the every block. In this proposed method, the SFs are optimized using GA to obtain maximum transparency. Experimental results indicate that the method reached the highest possible transparency. Fragility of the watermark under various attacks such as rotating, rescaling and sharpening is tested. When an attack does not occur, exactly the original extracted watermark is obtained; on the other hand, the extracted watermark is intensely distorted.

Evaluation of criterion functions for the fusion of multi-focus noisy images

Digital images are distorted by impulsive noise (IN) which caused by image sensors and communication channels in many practical applications. This noise may cause miscalculation of sharpness values in image fusion applications. In this paper, traditional criterion functions and frequency selective median filter (FSWM) based criterion function are evaluated for the fusion of multi-focus images in the presence of IN. Experimental results show that the FSWM filter is better than the other focus measures.

Multi-focus image fusion based on optimal defocus estimation ☆

Abstract One of the main drawbacks of the imaging systems is limited depth of field which prevents them from obtaining an all-in-focus image of the environment. This paper presents an efficient, pixel-based multi-focus image fusion method which generates an all-in-focus image by combining the images that are acquired from the same point of view with different focus settings. The proposed method first estimates the point spread function of each source image by utilizing the Levenberg–Marquardt algorithm. Then, artificially blurred versions of the source images are computed by convolving them with the estimated point spread functions. Fusion map is computed by making use of both the source and the artificially blurred images. At last, the fusion map is improved by morphological operators. Experimental results show that the proposed method is computationally competitive with the state-of-the-art methods and outperforms them in terms of both visual and quantitative metric evaluations.