4 research outputs found

    An importance driven genetic algorithm for the halftoning process

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    Most evolutionary approaches to halftoning techniques have been concerned with the paramount goal of halftoning: achieving an accurate reproduction of local grayscale intensities while avoiding the introduction of artifacts. A secondary concern in halftoning has been the preservation of edges in the halftoned image. In this paper, we will introduce a new evolutionary approach through the use of an importance function. This approach has at least two main characteristics. First, it can produce results similar to many other halftoning techniques. Second, if the chosen importance function is accordingly changed, areas of the image with high variance can be highlighted.III Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

    An importance driven genetic algorithm for the halftoning process

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    Most evolutionary approaches to halftoning techniques have been concerned with the paramount goal of halftoning: achieving an accurate reproduction of local grayscale intensities while avoiding the introduction of artifacts. A secondary concern in halftoning has been the preservation of edges in the halftoned image. In this paper, we will introduce a new evolutionary approach through the use of an importance function. This approach has at least two main characteristics. First, it can produce results similar to many other halftoning techniques. Second, if the chosen importance function is accordingly changed, areas of the image with high variance can be highlighted.III Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

    Grayscale Digital Halftoning Using Optimization Techniques

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    In this paper a complete outline of advanced Digital halftoning techniques is given. This paper explains halftoning from its definition, application of halftoning to different useful techniques which are improved to give a high signal to noise ratio image. Apart from signal to noise ratio another parameter which measures the similarity between two images is also shown in this paper. Additionally the drawback of each method and comparison of the SNR, and SSIM of all methods is also shown in this paper. Error diffusion technique using FS, Stucki and JJN filters is an efficient approach to halftoning. Its main drawback is that it undergoes linear distortion. This paper is completely describing the error diffusion method and the improvements made to error diffusion so as to get a well-defined and a visually pleasing halftone image. However an evolutionary algorithms called as particle swarm optimization and Genetic Algorithm are used to create filters for the image block wise and comparing with that of the image and then finally reconstructing the whole image. In these methods of PSO and GA the cost function is formulated based on the SSIM, average minority pixel distance and the string with the best cost function value is sorted using the Evolutionary algorithms. As the human eye acts as a spatial low pass filter the image which is to be halftoned is filtered through any visual model such as a HVS model and then undergoes through the process of evolutionary algorithms

    Halftone Image Generation with Improved Multiobjective Genetic Algorithm

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    A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images. Recently, the two major drawbacks of this conventional halftoning technique with GAs, i.e. it uses a substantial amount of computer memory and processing time, have been overcome by using an improved GA (GA-SRM) that applies genetic operators in parallel putting them in a cooperative/competitive stand with each other. The halftoning problem is a true multiobjective optimization problem. However, so far, the GA based halftoning techniques have treated the problem as a single objective optimization problem. In this work, the improved GA-SRM is extended to a multiobjective optimization GA to generate simultaneously halftone images with various combinations of gray level and spatial resolution. Simulation results verify that the proposed scheme can effectively generate several high quality images simultaneously in a single run reducing even further the overall processing time
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