尺度不变特征变换
人工智能
计算机视觉
滤波器(信号处理)
图像融合
计算机科学
模式识别(心理学)
融合
数学
图像(数学)
稳健性(进化)
特征(语言学)
语言学
哲学
作者
Naila Hayat,Muhammad Imran
标识
DOI:10.1016/j.jvcir.2019.06.002
摘要
Abstract A ghost-free multi-exposure image fusion technique using the dense SIFT descriptor and the guided filter is proposed in this paper. The results suggest that the presented scheme produces high-quality images using ordinary cameras and that too without the ghosting artifact. To do so, the dense SIFT descriptor is used to extract the local contrast information from source images. Whereas, for the dynamic scenes, the histogram equalization and median filtering are used to calculate the color dissimilarity feature. Three weighting terms: local contrast, brightness, and color dissimilarity feature are used to estimate the initial weights. The estimated initial weights contain discontinuities. Therefore, the guided filter is used to remove the noise and discontinuity in initial weights. Finally, the fusion is performed using a pyramid decomposition method. Experimental results prove the superiority of the proposed technique over existing state-of-the-art methods in terms of both subjective and objective evaluation.
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