Hardware-Friendly Laplacian-Based Multi-Focus Image Fusion in DCT Domain for Visual Sensor Network

作     者：Dhara, Sobhan Kanti Sen, Debashis Swamy, M. N. S. 

作者机构：IIT Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：165720-165739页

核心收录：

基　　金：Natural Sciences and Engineering Research Council of Canada Shastri Indo-Canadian Institute 

主　　题：Anti-diagonal matrix discrete cosine transform multi-focus image fusion visual sensor network 

摘      要：Visual sensor network (VSN) requires a multi-focus image or video frame fusion technique involving focus measure computation in the DCT-domain to generate an all-in-focus image. Such techniques are implemented on resource-constrained on-board systems requiring hardware-friendly implementations. In this article, we first show that components of the Laplacian matrix are related to the discrete cosine transform (DCT) basis. The relation is that the eigenvalues of the Laplacian with proper boundary condition form the diagonal elements of the diagonal matrix generated by the DCT operation on the Laplacian. Exploiting this relation, we propose a focus measure which works on the DCT coefficients reflecting the spatial-domain Laplacian operation. Certain simplifications allow our focus measure computation through hardware-friendly integer multiplication and summation, where matrix multiplication involves just N scalar multiplications for an N x N 2D signal. Finally, we propose an approach which suitably fuses multi-focus images or video frames in DCT based image or video coding framework through detection of properly focused area and neighborhood consistency analysis. We show that our proposed approach is hardware-friendly, computationally simple, and is fast enough for VSN. Through experimental results, we show that our approach outperforms the relevant state-of-the-art in multi-focus image fusion for VSN both quantitatively and subjectively. We also show that our approach is effective in comparison to the state-of-the-art and a few latest generic multi-focus image fusion techniques in terms of quantitative and subjective evaluations.