电子学报 ›› 2023, Vol. 51 ›› Issue (2): 416-426.DOI: 10.12263/DZXB.20210214
唐利明1, 熊点华1,2, 方壮1
收稿日期:
2021-02-03
修回日期:
2022-05-05
出版日期:
2023-02-25
作者简介:
基金资助:
TANG Li-ming1, XIONG Dian-hua1,2, FANG Zhuang1
Received:
2021-02-03
Revised:
2022-05-05
Online:
2023-02-25
Published:
2023-04-14
Supported by:
摘要:
图像成像过程中,由于空气消光性的影响,获取的数字图像质量会退化,包括灰度不均,对比度下降等,给图像分割或者目标的识别带来困难.为解决此问题,本文提出了一个基于比尔朗伯光吸收定律的变分水平集模型以实现此类退化图像的分割和修正.首先基于比尔朗伯定律,将观测图像建模为一个退化场和真实图像的乘积.然后对退化场进行Markov随机场正则化,对真实图像实施分片Gaussian分布拟合建模,结合水平集函数正则项,建立变分水平集模型.最后采用结合梯度下降的交替迭代算法对模型进行数值求解.实验结果表明,本文模型可以很好地排除退化场的影响,得到满意的图像分割和修正效果.和几个经典的变分图像分割模型相比,本文模型展示出较好的实验效果,具有最优的JSI,DSI和VOE指标值.
中图分类号:
唐利明, 熊点华, 方壮. 基于比尔朗伯定律的变分水平集模型[J]. 电子学报, 2023, 51(2): 416-426.
Li-ming TANG, Dian-hua XIONG, Zhuang FANG . A Variational Level Set Model Based on Beer-Lambert Law[J]. Acta Electronica Sinica, 2023, 51(2): 416-426.
图像 | LBF模型 | LIF模型 | LGDF模型 | 本文模型 | ||||
---|---|---|---|---|---|---|---|---|
Iter | Time | Iter | Time | Iter | Time | Iter | Time | |
合成图像A( | 130 | 8.23 | 130 | 6.43 | 150 | 9.42 | 50 | 2.52 |
合成图像B( | 120 | 7.62 | 110 | 5.46 | 150 | 9.36 | 40 | 2.02 |
汽车图像( | 60 | 3.70 | 60 | 2.87 | 70 | 4.41 | 50 | 2.51 |
脑MR图像( | 130 | 8.43 | 90 | 4.46 | 120 | 7.54 | 40 | 2.03 |
At本文图像( | 40 | 2.53 | 30 | 1.49 | 30 | 1.78 | 20 | 1.04 |
血管图像( | 1 420 | 89.90 | 860 | 42.24 | 1 530 | 96.28 | 148 | 7.32 |
表1 本文模型与LBF模型、 LIF模型、 LGDF模型的分割效率对比结果
图像 | LBF模型 | LIF模型 | LGDF模型 | 本文模型 | ||||
---|---|---|---|---|---|---|---|---|
Iter | Time | Iter | Time | Iter | Time | Iter | Time | |
合成图像A( | 130 | 8.23 | 130 | 6.43 | 150 | 9.42 | 50 | 2.52 |
合成图像B( | 120 | 7.62 | 110 | 5.46 | 150 | 9.36 | 40 | 2.02 |
汽车图像( | 60 | 3.70 | 60 | 2.87 | 70 | 4.41 | 50 | 2.51 |
脑MR图像( | 130 | 8.43 | 90 | 4.46 | 120 | 7.54 | 40 | 2.03 |
At本文图像( | 40 | 2.53 | 30 | 1.49 | 30 | 1.78 | 20 | 1.04 |
血管图像( | 1 420 | 89.90 | 860 | 42.24 | 1 530 | 96.28 | 148 | 7.32 |
图像 | LIC模型 | LSACM模型 | 本文模型 | |||
---|---|---|---|---|---|---|
Iter | Time | Iter | Time | Iter | Time | |
Horse( | 120 | 8.03 | 150 | 10.12 | 50 | 2.61 |
Plane( | 160 | 10.73 | 210 | 14.11 | 60 | 3.25 |
Wire( | 130 | 8.72 | 140 | 9.35 | 50 | 2.62 |
脑MR图像( | 90 | 6.12 | 110 | 7.46 | 40 | 2.09 |
表2 本文模型与LIC模型、 LSACM模型的效率对比结果
图像 | LIC模型 | LSACM模型 | 本文模型 | |||
---|---|---|---|---|---|---|
Iter | Time | Iter | Time | Iter | Time | |
Horse( | 120 | 8.03 | 150 | 10.12 | 50 | 2.61 |
Plane( | 160 | 10.73 | 210 | 14.11 | 60 | 3.25 |
Wire( | 130 | 8.72 | 140 | 9.35 | 50 | 2.62 |
脑MR图像( | 90 | 6.12 | 110 | 7.46 | 40 | 2.09 |
数据集 | 指标 | CV模型 | LBF模型 | LGDF模型 | MCV模型 | GLFIF模型 | GLIF模型 | 本文模型 |
---|---|---|---|---|---|---|---|---|
Image 1 | JSI | 0.732 5 | 0.704 3 | 0.713 9 | 0.638 6 | 0.711 1 | 0.650 6 | 0.743 2 |
DSI | 0.845 6 | 0.826 5 | 0.833 0 | 0.779 4 | 0.831 2 | 0.788 3 | 0.852 0 | |
VOE | 0.294 1 | 0.287 4 | 0.293 2 | 0.210 4 | 0.329 0 | 0.234 7 | 0.128 1 | |
Image 2 | JSI | 0.403 2 | 0.373 2 | 0.348 0 | 0.362 6 | 0.371 3 | 0.414 3 | 0.623 2 |
DSI | 0.465 3 | 0.429 1 | 0.291 6 | 0.416 0 | 0.426 8 | 0.478 3 | 0.732 1 | |
VOE | 0.556 7 | 0.417 8 | 0.461 9 | 0.545 4 | 0.520 8 | 0.422 8 | 0.147 6 | |
Image 3 | JSI | 0.732 5 | 0.704 3 | 0.713 9 | 0.638 6 | 0.711 1 | 0.650 6 | 0.753 4 |
DSI | 0.845 6 | 0.826 5 | 0.833 0 | 0.779 4 | 0.831 2 | 0.788 3 | 0.862 3 | |
VOE | 0.294 1 | 0.287 4 | 0.293 2 | 0.210 4 | 0.329 0 | 0.134 7 | 0.119 8 | |
Weizmann | JSI | 0.563 5 | 0.439 6 | 0.399 0 | 0.521 8 | 0.570 2 | 0.548 5 | 0.603 2 |
DSI | 0.693 9 | 0.597 5 | 0.402 4 | 0.657 4 | 0.701 1 | 0.686 6 | 0.711 5 | |
VOE | 0.402 2 | 0.288 5 | 0.318 1 | 0.418 6 | 0.369 8 | 0.336 3 | 0.276 5 |
表3 模型在Weizmann数据集中分割结果的JSI,DSI和VOE值
数据集 | 指标 | CV模型 | LBF模型 | LGDF模型 | MCV模型 | GLFIF模型 | GLIF模型 | 本文模型 |
---|---|---|---|---|---|---|---|---|
Image 1 | JSI | 0.732 5 | 0.704 3 | 0.713 9 | 0.638 6 | 0.711 1 | 0.650 6 | 0.743 2 |
DSI | 0.845 6 | 0.826 5 | 0.833 0 | 0.779 4 | 0.831 2 | 0.788 3 | 0.852 0 | |
VOE | 0.294 1 | 0.287 4 | 0.293 2 | 0.210 4 | 0.329 0 | 0.234 7 | 0.128 1 | |
Image 2 | JSI | 0.403 2 | 0.373 2 | 0.348 0 | 0.362 6 | 0.371 3 | 0.414 3 | 0.623 2 |
DSI | 0.465 3 | 0.429 1 | 0.291 6 | 0.416 0 | 0.426 8 | 0.478 3 | 0.732 1 | |
VOE | 0.556 7 | 0.417 8 | 0.461 9 | 0.545 4 | 0.520 8 | 0.422 8 | 0.147 6 | |
Image 3 | JSI | 0.732 5 | 0.704 3 | 0.713 9 | 0.638 6 | 0.711 1 | 0.650 6 | 0.753 4 |
DSI | 0.845 6 | 0.826 5 | 0.833 0 | 0.779 4 | 0.831 2 | 0.788 3 | 0.862 3 | |
VOE | 0.294 1 | 0.287 4 | 0.293 2 | 0.210 4 | 0.329 0 | 0.134 7 | 0.119 8 | |
Weizmann | JSI | 0.563 5 | 0.439 6 | 0.399 0 | 0.521 8 | 0.570 2 | 0.548 5 | 0.603 2 |
DSI | 0.693 9 | 0.597 5 | 0.402 4 | 0.657 4 | 0.701 1 | 0.686 6 | 0.711 5 | |
VOE | 0.402 2 | 0.288 5 | 0.318 1 | 0.418 6 | 0.369 8 | 0.336 3 | 0.276 5 |
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