مقاله انگلیسی رایگان در مورد ترکیب تصویر چند فوکوسی مبتنی بر شبکه – IEEE 2019
مشخصات مقاله | |
ترجمه عنوان 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 |
انتشار | مقاله سال ۲۰۱۹ |
تعداد صفحات مقاله انگلیسی | ۲۱ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۴٫۶۴۱ در سال ۲۰۱۸ |
شاخص H_index | ۵۶ در سال ۲۰۱۹ |
شاخص SJR | ۰٫۶۰۹ در سال ۲۰۱۸ |
شناسه ISSN | ۲۱۶۹-۳۵۳۶ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۱۸ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر، مهندسی فناوری اطلاعات |
گرایش های مرتبط | شبکه های کامپیوتری |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | College of Electronic and Information Engineering, Hebei University, Baoding 071000, China |
کلمات کلیدی | 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 |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2947378 |
کد محصول | E13879 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
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 |
بخشی از متن 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. |