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Target based image fusion using multi-scale feature selection in three regions

机译:基于目标的图像融合,在三个区域中使用多尺度特征选择

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摘要

Image captured by low dynamic range (LDR) camera fails to capture entire exposure level of scene, and instead only covers certain range of exposures. In order to cover entire exposure level in single image, bracketed exposure LDR images are combined. The range of exposures in different images results in information loss in certain regions. These regions need to be addressed and based on this motive a novel methodology of layer based fusion is proposed to generate high dynamic range image. High and low-frequency layers are formed by dividing each image based on pixel intensity variations. The regions are identified based on information loss section created in differently exposed images. High-frequency layers are combined using region based fusion with Dense SIFT which is used as activity level testing measure. Low-frequency layers are combined using weighted sum. Finally combined high and low-frequency layers are merged together on pixel to pixel basis to synthesize fused image. Objective analysis is performed to compare the quality of proposed method with state-of-the-art. The measures indicate superiority of the proposed method.
机译:低动态范围(LDR)摄像机捕获的图像无法捕获整个曝光场景,而是仅涵盖某些曝光范围。为了在单个图像中覆盖整个曝光率,组合括号曝光LDR图像。不同图像中的曝光范围导致某些区域中的信息丢失。需要解决这些区域,并根据该动机基于基于层的融合的新方法,以产生高动态范围图像。通过基于像素强度变化来除以每个图像来形成高和低频层。该区域是基于在不同暴露的图像中创建的信息丢失部分来识别的区域。使用基于区域的融合组合使用具有致密筛选的高频层,其用作活动水平测试测量。使用加权和相结合低频层。最后,将高和低频层组合在一起以像素为像素的基础,以合成融合图像。进行客观分析以比较所提出的方法的质量。措施表示提出的方法的优势。

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