ترکیب تصویر چند فوکوسی مبتنی بر شبکه
ترجمه نشده

ترکیب تصویر چند فوکوسی مبتنی بر شبکه

عنوان فارسی مقاله: ترکیب تصویر چند فوکوسی مبتنی بر شبکه باقی مانده در دامنه شارلت غیر زیر نمونه ای
عنوان انگلیسی مقاله: Multi-Focus Image Fusion Based on Residual Network in Non-Subsampled Shearlet Domain
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری
کلمات کلیدی فارسی: ترکیب تصویر، ترکیب تصویر چند فوکوسی، تبدیل شارلت غیر زیر نمونه ای (NSST)، شبکه باقی مانده (ResNet)
کلمات کلیدی انگلیسی: Image fusion, multi-focus image fusion, NSST, ResNet
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2947378
دانشگاه: College of Electronic and Information Engineering, Hebei University, Baoding 071000, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 21
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13879
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Non-Subsampled Shearlet Transform


III. Fusion Rules Based on ResNet-50 Model


IV. Fusion Rules for ResNet Based on NSST Change Domain


V. Experiment and Analysis


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


In order to obtain a panoramic image which is clearer, and has more layers and texture features, we propose an innovative multi-focus image fusion algorithm by combining with non-subsampled shearlet transform (NSST) and residual network (ResNet). First, NSST decomposes a pair of input images to produce subband coefficients of different frequencies for subsequent feature processing. Then, ResNet is applied to fuse the low frequency subband coefficients, and improved gradient sum of Laplace energy (IGSML) perform high frequency feature information processing. Finally, the inverse NSST is performed on the fused coefficients of different frequencies to obtain the final fused image. In our method, we fully consider the low frequency global features and high frequency detail information in image by using NSST. For low-frequency coefficients fusion, we can also obtain the spatial information features of low-frequency coefficient images by using ResNet, which has a deep network structure. IGSML can use different directional gradients to process high-frequency subband coefficients of different levels and directions, which is more conducive to the fusion of the coefficients. The experiment results show that the proposed method has been improved in the structural features and edge texture in the fusion images.


Introduction


In the field of digital image processing, different imaging devices acquire different information from the same scene. As in the optical lens, the acquired image is not an allfocus image since the limited of the lens depth range. The optical image is only clear in the part of the scene that is focused in the lens range, and the rest is a blurred defocused image. Typically, image fusion is often used to produce good results which is superior to the original image quality [1], [2]. The fused image contains more scene information, which is more suitable for imaging features of the human eye and is also convenient for later computer processing. Therefore, the process of multi-focus image fusion can be considered as a tool for producing high quality result images [3], [4]. In the development of multi-focus image fusion, there are two types of fusion methods, namely spatial domain fusion and transform domain fusion [5]. However, the most important aspect is the design of fusion rules in the image fusion processing. The image fusion methods based on transform domain are a popular and widely in this fields. In transform domain-based fusion algorithm, the multi-scale decomposition of the original images is mainly applied by multiscale transform (MST), and image fusion is performed by using different fusion rules for image coefficients at different scales. In image fusion based on transform domain, the performance of the algorithm is mainly dependent on the choice of transform domain and the design of fusion rules.

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