Title of Paper:Multi-Focus Image Fusion With a Natural Enhancement via a Joint Multi-Level Deeply Supervised Convolutional Neural Network

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First Author:Zhao, Wenda

Correspondence Author:Zhao, WD (reprint author), Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China.

Co-author:Wang, Dong,Lu, Huchuan

Date of Publication:2019-04-01

Journal:IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

Included Journals:SCIE、EI

Volume:29

Issue:4

Page Number:1102-1115

ISSN No.:1051-8215

Key Words:Multi-focus image fusion with enhancement; end-to-end CNN architecure; joint multi-level feature; deep supervision

Abstract:Common non-focused areas are often present in multi-focus images due to the limitation of the number of focused images. This factor severely degrades the fusion quality of multi-focus images. To address this problem, we propose a novel end-to-end multi-focus image fusion with a natural enhancement method based on deep convolutional neural network (CNN). Several end-to-end CNN architectures that are specifically adapted to this task are first designed and researched. On the basis of the observation that low-level feature extraction can capture low-frequency content, whereas high-level feature extraction effectively captures high-frequency details, we further combine multi-level outputs such that the most visually distinctive features can be extracted, fused, and enhanced. In addition, the multi-level outputs are simultaneously supervised during training to boost the performance of image fusion and enhancement. Extensive experiments show that the proposed method can deliver superior fusion and enhancement performance than the state-of-the-art methods in the presence of multi-focus images with common non-focused areas, anisotropic blur, and misregistration.