Spatial super resolution based image reconstruction using HIBP

R. Nayak, S. Monalisa, D. Patra
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引用次数: 8

Abstract

Spatial image resolution explains about the pixel density in a digital image. As a result more the number of pixels more detailed visibility of information contained in the image. Hardware limitations restrict the increase in number of sensor elements per unit area in camera. Therefore an imaging system with inadequate sensor array will generate low resolution image which causes pixelization effect in them. This problem is solved in software level using signal processing techniques called super resolution based image reconstruction. In this paper super resolution based image reconstruction problem is addressed, which is used for resolution enhancement. Unlike interpolation, it takes information from multiple number of low resolution images with sub-pixel shifts and contain nonredundant data to generate a high resolution image. In this proposed reconstruction method, a hybrid iterative back projection technique is developed exploiting the notion of cuckoo search optimization algorithm in iterative back projection method. The high resolution solution from iterative back projection method is optimized using Cuckoo optimization algorithm. The performance of the proposed algorithm is found to be outperforming that of existing IBP and other interpolation based reconstruction techniques.
基于HIBP的空间超分辨率图像重建
空间图像分辨率解释了数字图像中的像素密度。因此,像素数越多,图像中所含信息的详细可见性越高。硬件限制限制了相机中每单位面积传感器元素数量的增加。因此,传感器阵列不合适的成像系统会产生低分辨率的图像,从而产生像素化效应。这一问题在软件层面得到了解决,采用了基于超分辨率图像重建的信号处理技术。本文研究了基于超分辨率的图像重建问题,并将其用于图像的分辨率增强。与插值不同,它从多个具有亚像素位移的低分辨率图像中获取信息,并包含非冗余数据以生成高分辨率图像。在该方法中,利用迭代反投影法中的布谷鸟搜索优化算法的思想,提出了一种混合迭代反投影技术。采用布谷鸟优化算法对迭代反投影法的高分辨率解进行优化。该算法的性能优于现有的IBP和其他基于插值的重建技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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