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基于 POSHE 的最优裁剪限制超声测井图像对比度增强方法。

A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images.

机构信息

Electronics and Information School, Yangtze University, Jingzhou 434023, China.

National Demonstration Center for Experimental Electrical & Electronic Education, Yangtze University, Jingzhou 434023, China.

出版信息

Sensors (Basel). 2018 Nov 15;18(11):3954. doi: 10.3390/s18113954.

DOI:10.3390/s18113954
PMID:30445698
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6263424/
Abstract

Enabled by piezoceramic transducers, ultrasonic logging images often suffer from low contrast and indistinct local details, which makes it difficult to analyze and interpret geologic features in the images. In this work, we propose a novel partially overlapped sub-block histogram-equalization (POSHE)-based optimum clip-limit contrast enhancement (POSHEOC) method to highlight the local details hidden in ultrasonic well logging images obtained through piezoceramic transducers. The proposed algorithm introduces the idea of contrast-limited enhancement to modify the cumulative distribution functions of the POSHE and build a new quality evaluation index considering the effects of the mean gradient and mean structural similarity. The new index is designed to obtain the optimal clip-limit value for histogram equalization of the sub-block. It makes the choice of the optimal clip-limit automatically according to the input image. Experimental results based on visual perceptual evaluation and quantitative measures demonstrate that the proposed method yields better quality in terms of enhancing the contrast, emphasizing the local details while preserving the brightness and restricting the excessive enhancement compared with the other seven histogram equalization-based techniques from the literature. This study provides a feasible and effective method to enhance ultrasonic logging images obtained through piezoceramic transducers and is significant for the interpretation of actual ultrasonic logging data.

摘要

基于压电阻变陶瓷换能器的超声测井图像通常对比度低,局部细节不清晰,这使得分析和解释图像中的地质特征变得困难。在这项工作中,我们提出了一种新的基于部分重叠子块直方图均衡化(POSHE)的最优裁剪限制对比度增强(POSHEOC)方法,以突出压电阻变陶瓷换能器获取的超声测井图像中隐藏的局部细节。所提出的算法引入了对比度限制增强的思想来修改 POSHE 的累积分布函数,并构建了一个新的质量评估指标,该指标考虑了平均梯度和平均结构相似性的影响。新的指标旨在为子块的直方图均衡化获得最优的裁剪限制值。它根据输入图像自动选择最优的裁剪限制值。基于视觉感知评估和定量度量的实验结果表明,与文献中的其他七种基于直方图均衡化的技术相比,该方法在增强对比度、强调局部细节、同时保持亮度和限制过度增强方面具有更好的性能。这项研究为增强压电阻变陶瓷换能器获取的超声测井图像提供了一种可行且有效的方法,对实际超声测井数据的解释具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/55fd580cd225/sensors-18-03954-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/29a457be71b6/sensors-18-03954-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/260e51fab91a/sensors-18-03954-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/6bafc6f72a2c/sensors-18-03954-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/9053c3e27a9c/sensors-18-03954-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/9a3ac9c8ea14/sensors-18-03954-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/de601d804750/sensors-18-03954-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/e7a3fd5bc2ac/sensors-18-03954-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/703587036e54/sensors-18-03954-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/55fd580cd225/sensors-18-03954-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/29a457be71b6/sensors-18-03954-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/260e51fab91a/sensors-18-03954-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/6bafc6f72a2c/sensors-18-03954-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/9053c3e27a9c/sensors-18-03954-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/9a3ac9c8ea14/sensors-18-03954-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/de601d804750/sensors-18-03954-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/e7a3fd5bc2ac/sensors-18-03954-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/703587036e54/sensors-18-03954-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07bc/6263424/55fd580cd225/sensors-18-03954-g009a.jpg

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