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Target Object Recognition Using Multiresolution SVD and Guided Filter with Convolutional Neural Network
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2019-12-13 , DOI: 10.1142/s0218001420520084
Biswajit Biswas 1 , Swarup Kr Ghosh 2 , Anupam Ghosh 3 , Chandan Chakraborty 4 , Pabitra Mitra 5
Affiliation  

To design an efficient fusion scheme for the generation of a highly informative fused image by combining multiple images is still a challenging task in computer vision. A fast and effective image fusion scheme based on multi-resolution singular value decomposition (MR-SVD) with guided filter (GF) has been introduced in this paper. The proposed scheme decomposes an image of two-scale by MR-SVD into a lower approximate layer and a detailed layer containing the lower and higher variations of pixel intensity. It generates lower and details of left focused (LF) and right focused (RF) layers by applying the MR-SVD on each series of multi-focus images. GF is utilized to create a refined and smooth-textured weight fusion map by the weighted average approach on spatial features of the lower and detail layers of each image. A fused image of LF and RF has been achieved by the inverse MR-SVD. Finally, a deep convolutional autoencoder (CAE) has been applied to segment the fused results by generating the trained-patches mechanism. Comparing the results by state-of-the-art fusion and segmentation methods, we have illustrated that the proposed schemes provide superior fused and its segment results in terms of both qualitatively and quantitatively.

中文翻译:

使用多分辨率 SVD 和带有卷积神经网络的引导滤波器的目标对象识别

设计一种有效的融合方案,通过组合多张图像来生成信息量丰富的融合图像,仍然是计算机视觉中的一项具有挑战性的任务。本文介绍了一种基于多分辨率奇异值分解(MR-SVD)和引导滤波器(GF)的快速有效的图像融合方案。所提出的方案通过MR-SVD将两尺度图像分解为一个较低的近似层和一个包含像素强度较低和较高变化的详细层。它通过在每一系列多焦点图像上应用 MR-SVD 来生成左聚焦 (LF) 和右聚焦 (RF) 层的下部和细节。GF用于通过加权平均方法对每个图像的较低层和细节层的空间特征进行加权平均来创建精细且纹理平滑的权重融合图。通过逆 MR-SVD 获得了 LF 和 RF 的融合图像。最后,深度卷积自动编码器 (CAE) 已被应用于通过生成经过训练的补丁机制来分割融合结果。通过最先进的融合和分割方法比较结果,我们已经说明,所提出的方案在定性和定量方面都提供了优越的融合及其分割结果。
更新日期:2019-12-13
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