王运生,王黎明,聂芬.基于焦点度量与模糊逻辑的多聚焦图像融合算法[J].包装工程,2018,39(13):208-215.
WANG Yun-sheng,WANG Li-ming,NIE Fen.The Multi Focus Image Fusion Algorithm Based on Focus Measurement and Fuzzy Logic[J].Packaging Engineering,2018,39(13):208-215. |
| 基于焦点度量与模糊逻辑的多聚焦图像融合算法 |
| The Multi Focus Image Fusion Algorithm Based on Focus Measurement and Fuzzy Logic |
| 投稿时间：2017-12-13 修订日期：2018-07-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.13.034 |
| 中文关键词: 多聚焦图像融合 模糊逻辑 空间频率 改进的Laplacian能量和 梯度和 焦点三态图 |
| 英文关键词: multi focus image fusion fuzzy logic spatial frequency improved sum-modified Laplacian sum of gradient focus tri-state map |
| 基金项目:国家自然科学基金（61540007）；山西省教育厅自然科学技术研究项目（GH-16213） |
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| 中文摘要: |
| 目的 为了使多聚焦灰度图像融合时保持源图像清晰信息，并有效抑制块效应和重影现象，基于3种不同聚焦测量与模糊推理系统，设计一种焦点度量与模糊逻辑的多聚焦灰度图像融合方案。方法 首先，分别利用空间频率（SF）、改进的Laplacian能量和（SML），以及梯度和（SOG）计算输入灰度图像在像素邻域的局部焦点特征，并利用像素相关性改善对比度，从而得到SF, SML, SOG等3种聚焦度量。其次，根据SF与SML强度关系，建立焦点三态图，结合互补聚焦信息，并进行形态、中值滤波和一致性检查，消除狭窄的鸿沟和突起问题。然后，引入模糊逻辑算子，将每个图像像素的SF, SML图以及SOG作为模糊化的输入，通过模糊规则和去模糊器，生成每个图像的融合权重。最后，根据焦点权重执行加权融合，形成最后的融合图像。结果 实验结果表明，与当前流行的融合方案相比，对于灰度图像，所提算法在融合性能上具有一定的优势，其融合图像具有更好的景深信息，避免了块效应与重影现象。结论 所提算法具有良好的融合质量，能够有效提高灰度图像的分辨率，在图像处理领域具有一定的价值。 |
| 英文摘要: |
| The work aims to design a multi focus image fusion scheme with focus measurement and fuzzy logic based on three different focusing measurement and fuzzy inference systems, to keep clear source image information and effectively suppress block effect and ghost phenomenon in multi focus gray image fusion. Firstly, the Spatial Frequency (SF), the improved Sum-Modified Laplacian (SML) and Sum of Gradient (SOG) were respectively used to calculate the local focus feature of the input gray image in pixel neighborhood, and the pixel correlation was used to improve the contrast, thus getting three kinds of focusing measurements of SF, SML and SOG. Secondly, according to the intensity relationship between SF and SML, the focus tri-state map was established, which combined complementary focus information and performed morphological, median filtering and consistency checking to eliminate narrow gap and protuberance. Then, the fuzzy logic operator was introduced, the SF, SML and SOG of each pixel were taken as fuzzy inputs, and the fusion weights of each image were generated by fuzzy rules and defuzzer. At last, the final fused image was formed on the basis of the weight fusion performed by the focus weight. The experimental results showed that, compared with the current popular fusion schemes, for the gray image, the proposed algorithm had some advantages in fusion performance, and the fused image had better depth information, avoiding block effect and ghost phenomenon. The proposed algorithm has good fusion quality which can effectively improve the resolution of the gray image, and it has certain value in the field of image processing. |
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