计算机工程与应用2019，Vol.55Issue(4)：193-199,7.DOI:10.3778/j.issn.1002-8331.1711-0196
结合引导滤波的自适应多曝光图像融合
Adaptive Multi-Exposure Image Fusion with Guided Filtering
摘要
Abstract
In order to solve the phenomenon of halo and gradient inversion caused by guided filtering and the loss of edge of image fusion, this paper proposes a novel improved adaptive multi-exposure image fusion algorithm with guided filtering. Firstly, this paper sets the weight function according to the gradient information in the guided filtering, and combines the image pixel and the mean of the region to create the function, and realizes the adaption of the texture feature of different regions. Secondly, this paper sets the weight function by using the relationship between average brightness and contrast, saturation and exposure moderation, so that the weight value in the weighted average fusion process is no longer a fixed value, and it can adaptively adjust according to different image brightness, values are also different, making the fusion image quality better. Finally, the details of the original sequence map are superimposed on the improved guided filtering image, and the texture detail layer is constructed. The experimental results weaken the halo and gradient inversion phe-nomena, make the image more real and the details more clear, so the effect of image processing is better. The algorithm is superior to the multi-exposure fusion algorithm and the multi-exposure image fusion of the guided filter, which allevi-ates the halo phenomenon and obtains the highest 2.5%, 30% and 30% quality improvement respectively in the informa-tion entropy, mutual information and edge information evaluation.关键词
光晕/梯度反转/平均亮度/自适应参数调整/细节增强Key words
halo/ gradient inversion/ average brightness/ parameter self-adaption/ detail enhancement分类
信息技术与安全科学引用本文复制引用
谢伟,王莉明,胡欢君,涂志刚..结合引导滤波的自适应多曝光图像融合[J].计算机工程与应用,2019,55(4):193-199,7.基金项目
国家自然科学基金(No.61501198,No.41671377,No.41501463) （No.61501198,No.41671377,No.41501463）
湖北省自然科学基金面上项目(No.2014CFB461) （No.2014CFB461）
武汉市青年科技晨光计划(No.2014072704011248). （No.2014072704011248）
