کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6957925 | 1451922 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A multi-scale contrast-based image quality assessment model for multi-exposure image fusion
ترجمه فارسی عنوان
یک مدل ارزیابی کیفیت تصویر مبتنی بر کنتراست چند منظوره برای همگام سازی تصویر چندگانه
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کلمات کلیدی
ترکیب چندین تصویر قرار گرفتن در معرض، ارزیابی کیفیت تصویر، اندازه گیری شباهت کنتراست، طرح چند منظوره،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
In this paper, an accurate and efficient image quality assessment (IQA) model using the contrast information, called multi-scale contrast-based model (MCM), is proposed for conducting objective quality evaluation of multi-exposure image fusion (MEF). It is inspired by the fact that the human visual system (HVS) is highly sensitive to contrast that is naturally inherited in the MEF application. The key novelty of the proposed MCM lies in the usage of two salient contrast features, i.e., contrast structure and contrast saturation. For each reference and MEF images, the degree of similarity measured for each above-mentioned contrast attribute is then computed independently, followed by combining them together with the weight of each reference image computed based on its relevance to MEF image for obtaining contrast similarity maps (CSMs). Subsequently, all the obtained CSMs are fused using a standard deviation pooling strategy to generate the quality score. Finally, a multi-scale scheme is utilized to explore the image details from finer to coarser scales for producing the final MCM score. Simulation results have clearly shown that the proposed MCM model is more consistent with the perception of the HVS on the evaluation of MEF images than multiple state-of-the-art IQA methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 145, April 2018, Pages 233-240
Journal: Signal Processing - Volume 145, April 2018, Pages 233-240
نویسندگان
Lu Xing, Lei Cai, Huanqiang Zeng, Jing Chen, Jianqing Zhu, Junhui Hou,