摘要
现有的内窥镜图像高光移除算法往往存在移除结构不合理、颜色失真等情况,而这会导致病灶识别算法和图像增强算法产生错误的结果。为了解决以上问题,在高光定位方面,提出了一种基于暗区域内生长和Scharr滤波结合的相对高光定位方法;在高光填充方面,提出了一种改进的Crinminisi算法。首先,通过统计大量数据,限定搜索范围,从而提高填充效率;其次,改进优先权统计范围,以避免重复的无意义的计算;最后,针对不同区域的自适应模板,合理重建纹理。在选取不同人体组织的内窥镜图像数据集上进行实验,相较于基于双色反射模型的方法、自适应鲁棒主成分分析(RPCA)方法、基于热扩散的方法和原始Criminisi算法,所提算法的自然图像质量评估(NIQE)值均为最小;相较于RPCA方法、基于热扩散的方法和原始Crimnisi算法,所提算法的运行时间也均为最少。实验结果表明,所提算法不仅比其他算法具有更好的客观图像指标,而且相较于原始Criminisi算法在效率上有近百倍的提升。
The existing endoscopic image highlight removal algorithms often have some problems such as unreasonable removal structure and color distortion,which leads to the wrong results of the focus recognition algorithms and image enhancement algorithms.In order to solve the above problems,in the aspect of highlight localization,a method based on the combination of growth in dark region and Scharr filtering was proposed to locate relative highlight;in the aspect of highlight filling,an improved Crinminisi algorithm was proposed.Firstly,through the statistics on a huge amount of data,the search scope was limited and the filling efficiency was increased.Secondly,the statistical scope of priority was improved to avoid repeated meaningless calculations.Finally,the reasonable reconstruction of texture was performed according to the adaptive templates of different regions.Experiments were carried out on endoscopic image dataset of different human tissues,compared with the dichromatic reflection model based method,the Robust Principle Component Analysis(RPCA)method,the thermal diffusion method and the original Criminisi algorithm,the Natural Image Quality Evaluator(NIQE)value of the proposed algorithm was the lowest.Compared with the RPCA method,the thermal diffusion method and the original Crimnisi algorithm,the running time of the proposed algorithm was the lowest.Experimental results show that the proposed algorithm not only has better objective image indicators than other algorithms,but also has a nearly 100-fold improvement in efficiency compared to the original Criminisi algorithm.
作者
池月
李正平
徐超
冯博
CHI Yue;LI Zhengping;XU Chao;FENG Bo(School of Integrated Circuits,Anhui University,Hefei Anhui 230601,China)
出处
《计算机应用》
CSCD
北大核心
2023年第4期1278-1283,共6页
journal of Computer Applications
基金
国家重点研发计划项目(2019YFC0117800)。
