Multi-exposure Image Fusion using Patchbased Component Decomposition

Dharmika A, M. Gnanapriya
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Abstract

Multi exposure image fusion is always a challenge in task in image processing. The multiple images with the different image content, when mixed using a fusion formula generate different effects. One of the most prominent effects is ghosting effect. Ghost in effect occur even in capturing of images. The smallest ghosting effect may be treated as image blur. To handle ghosting effect as well as many other affects that are generated in the process of fusion are treated in the proposed technique. The proposal scheme introduces a completely new representation that may be explorer for the for many different applications. First the input images are decomposed into several patches. As the fusion involves multiple input images the special correlated patches are further grouped into a class. Individual patches of the class are decomposed into three logical components named strength structure and intensity. These components are calculated for all the patches of the class. Now using the rule of fusion these logical components are derived for the whole class. The decomposition of a patch into logical components is unique as well as invertible hence using the generated components patches restored. Simulation results prove the superiority of the scheme proposed.
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基于patch的分量分解的多曝光图像融合
多曝光图像融合一直是图像处理中的难题。具有不同图像内容的多幅图像,在使用融合公式混合时产生不同的效果。其中最突出的效果是重影效应。鬼影实际上甚至在捕捉图像时也会发生。最小的重影效果可能被视为图像模糊。为了处理重影效应以及在融合过程中产生的许多其他影响,提出的技术进行了处理。提议方案引入了一种全新的表示形式,可以用于许多不同的应用程序。首先,将输入图像分解成几个小块。当融合涉及多个输入图像时,特殊的相关补丁被进一步分组为一类。类的单个补丁被分解为强度、结构和强度三个逻辑分量。这些组件是为类的所有补丁计算的。现在使用融合规则,这些逻辑组件为整个类派生。将一个补丁分解为逻辑组件是唯一的,也是可逆的,因此使用生成的组件修复补丁。仿真结果证明了该方案的优越性。
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