融合Retinex框架对电子内镜图像的增强

陈晓冬; 席佳祺; 汪毅; 蔡怀宇; 孙刚

doi:10.3788/OPE.20192710.2241

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融合Retinex框架对电子内镜图像的增强

信息科学 | 更新时间：2020-08-13

- 融合Retinex框架对电子内镜图像的增强
- Enhancement of electronic endoscope image by fusing retinex frame
- 光学精密工程 2019年27卷第10期页码：2241-2250
- 作者机构：
  
  1.天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室，天津 300072
  2.中国人民解放军总医院，北京 100000
- 作者简介：
  
  [ "陈晓冬(1975-)，男，浙江人，教授，博士生导师，1996年、2002年于天津大学分别获得学士、博士学位，主要从事光电成像与检测技术方向的研究。E-mail：xdchen@tju.edu.cn" ]
  [ "席佳祺(1995-)，女，河南人，硕士研究生，2017年于天津大学获得学士学位，主要从事电子内镜图像处理系统的研究。E-mail：xijiaqi@tju.edu.cn" ]
- 基金信息：
  
  “十三五”支撑计划资助项目(2018YFC0116202);“十三五”支撑计划资助项目(2017YFC0109702);“十三五”支撑计划资助项目(2017YFC0109901)
- DOI：10.3788/OPE.20192710.2241
  中图分类号： TP394.1
- 收稿日期：2019-03-07，
  
  录用日期：2019-5-7，
  
  纸质出版日期：2019-10-15
- 稿件说明：
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陈晓冬, 席佳祺, 汪毅, 等. 融合Retinex框架对电子内镜图像的增强[J]. 光学精密工程, 2019,27(10):2241-2250.

Xiao-dong CHEN, Jia-qi XI, Yi WANG, et al. Enhancement of electronic endoscope image by fusing retinex frame[J]. Optics and precision engineering, 2019, 27(10): 2241-2250.
陈晓冬, 席佳祺, 汪毅, 等. 融合Retinex框架对电子内镜图像的增强[J]. 光学精密工程, 2019,27(10):2241-2250. DOI： 10.3788/OPE.20192710.2241.

Xiao-dong CHEN, Jia-qi XI, Yi WANG, et al. Enhancement of electronic endoscope image by fusing retinex frame[J]. Optics and precision engineering, 2019, 27(10): 2241-2250. DOI： 10.3788/OPE.20192710.2241.

摘要

针对目前图像增强算法对于电子内镜图像光照不均匀、低照度区域边缘细节不明显以及高噪声等问题的局限性，设计了一种用于电子内镜图像的融合低噪声、均衡光照和细节增强的Retinex框架，并根据此框架设计了增强算法。算法首先利用基于位置信息与相邻频率的滤波器得到低噪光照值；为了有效区分噪声与细节信息，设计了一种基于最大后验概率估计(Maximum A Posteriori estimation，MAP)的反射率估计方法，引入光照因子控制概率权重，对于低照度区域反射率平滑项施加强约束，并通过最大化其后验概率以应对低照度区域的高噪声问题；为均衡光照、应对人体内黏膜和消化液的散射和吸收导致的图像退化，基于暗通道先验(Dark Channel Prior，DCP)算法设计了反向均衡化模型以得到均衡光照值；为应对低照度区域细节信息不明显问题，利用对比度限制自适应直方图均衡化得到细节增强结果。通过使用均衡光照值补偿增强后的反射率，实现噪声抑制、光照均衡、细节增强提高之间的有效融合。实验结果表明，本算法较于近期的同类算法NIEIE(Non-uniform Illumination Endoscopic Imaging Enhancement)，能够在保持信息熵与峰值信噪比的基础上，增强度提升23.94%，对于电子内镜图像具有良好的适用性。

Abstract

In view of the limitation of current enhancement algorithms for the problems of nonuniform illumination

unobvious edge details

and image noise in electronic endoscope images

a Retinex-based framework for combining low noise

balanced illumination

and detail enhancement of electronic endoscope images was proposed

and an enhancement algorithm was designed according to this framework. The algorithm used an illumination filter to obtain the illumination estimation

with the noise removed. To effectively distinguish noise from detailed information to obtain a more accurate reflectance

first

a reflectance estimation method was designed based on Maximum A Posterior estimation (MAP). The illumination factor was designed according to the illumination estimation to control the probability weight. The smoothness term of reflectance in a low illumination area was subjected to a strong constraint

and the posterior probability was maximized to cope with noise interference caused by nonuniform illumination. Second

a reverse equalization model was designed according to the Dark Channel Prior (DCP) algorithm to deal with local image degradation caused by nonuniform illumination and by the scattering and absorption of mucosa and digestive juice in the human body. As reflectance containing detailed information pays more attention to contrast

the contrast enhancement result was derived by using contrast-limited adaptive histogram equalization to deal with unobvious edge details. The final enhanced image was obtained by compensating the adjusted illumination back to the reflectance. Through this synthesis

the enhanced image represented a compromise between noise filtering

detail enhancement

and local contrast enhancement. The experimental results show that

compared with the average enhancement degree of the algorithm of Non-uniform Illumination Endoscopic Imaging Enhancement (NIEIE)

that of the algorithm proposed in this paper increase by 23.94% by maintaining the information entropy and peak signal-to-noise ratio. Therefore

the proposed algorithm has good applicability for electronic endoscope images.

关键词

Keywords

references

ZHANG K, YUAN B, WANG L. An image enhancement technique using nonlinear transfer function and unsharp masking in multispectral endoscope[J]. Proceedings of the Spie, 2017, 245:1024504.

KANSAL S, PURWAR S, TRIPATHI R K. Image contrast enhancement using unsharp masking and histogram equalization[J]. Multimedia Tools and Applications, 2018, 77(20):26919-26938.

冯清枝, 王丹.基于LIP模型和CLAHE的低照度图像增强算法[J].光电技术应用, 2018, 33(5).

FENG Q ZH, WANG D. A novel algorithm for low illumination image enhancement based on LIP and CLAHE[J]. Electro-Optic Technology Application, 2018, 33(5). (in Chinese)

YONG W, TING L, YONGSHENG Q. Image enhancement algorithm research based on the archives monitoring under low illumination[C]. IEEE International Conference on Electronic Measurement & Instruments. IEEE, 2016.

KONG T, ISA N. Enhancer-based contrast enhancement technique for non-uniform illumination and low-contrast images[J]. Multimedia Tools & Applications, 2017, 76(12):14305-14326.

PARK S, YU S, MOON B, et al .. Low-light image enhancement using variational optimization-based retinex model[J]. IEEE Transactions on Consumer Electronics, 2017, 63(2):178-184.

PARK S, YU S, KIM M, et al .. Dual autoencoder network for retinex-based low-light image enhancement[J]. IEEE Access, 2018:1-1.

KWOK N, SHI H, WU H, et al .. Logarithmic profile mapping multi-scale Retinex for restoration of low illumination images[C]. International Conference on Graphic & Image Processing. Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 2018.

赵宏宇, 肖创柏, 禹晶, 等.马尔科夫随机场模型下的Retinex夜间彩色图像增强[J].光学精密工程, 2014, 22(4):1048-1055.

ZHAO H Y, XIAO CH B, YU J, et al .. A Retinex algorithm for night color image enhancement by MRF[J]. Opt.Precision Eng., 2014, 22(4):1048-1055. (in Chinese)

LI M, LIU J, YANG W, et al .. Structure-revealing low-light image enhancement via robust retinex model[J]. IEEE Transactions on Image Processing, 2018:1-1.

JOBSON D J, RAHMAN Z, WOODELL G A. Properties and performance of a center/surround retinex[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 1997, 6(3):451-462.

JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing, 1997, 6(7):965-976.

CHEN S, BEGHDADI A. Natural rendering of color image based on retinex[C]. Image Processing (ICIP), 2009 16th IEEE International Conference on. IEEE, 2009.

KIMMEL R, ELAD M, SHAKED D, et al .. A variational framework for retinex[J]. International Journal of Computer Vision, 2003, 52(1):7-23.

WANG S, ZHENG J, HU H M, et al .. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Transactions on Image Processing, 2013, 22(9):3538-3548.

FU X, ZENG D, HUANG Y, et al .. A fusion-based enhancing method for weakly illuminated images[J]. Signal Processing, 2016:S0165168416300949.

RAO Z, XU T, LUO J, et al .. Non-uniform illumination endoscopic imaging enhancement via anti-degraded model and L1L2-based variational retinex[J]. EURASIP Journal on Wireless Communications and Networking, 2017, 2017(1):205.

CHU Y J, MAK C M. A new QR decomposition-based RLS algorithm using the split Bregman method for L1-regularized problems[J]. Signal Processing, 2016, 128:303-308.

MA W, OSHER S. A TV Bregman iterative model of Retinex theory[J]. Inverse Problems and Imaging (IPI), 2017, 6(4):697-708.

刘海波, 杨杰, 吴正平, 等.改进的基于雾气理论的视频去雾[J].光学精密工程, 2016, 24(7).

LIU H B, YANG J, WU ZH P, et al.. Improved video defogging based on fog theory[J]. Opt.Precision Eng., 2016, 24(7). (in Chinese)

HE K, SUN J, TANG X. Single image haze removal using dark channel prior.[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(12):2341-2353.

CHANG Y, JUNG C, KE P, et al .. Automatic contrast limited adaptive histogram equalization with dual gamma correction[J]. IEEE Access, 2018(99):1-1.

李赓飞, 李桂菊, 韩广良, 等.亮通道先验Retinex对低照度图像的光照补偿[J].光学精密工程, 2018, 26(5): 1191-1200.

LI G F, LI G J, HAN G L, et al .. Illumination compensation using Retinex model based on bright channel prior[J]. Opt.Precision Eng., 2018, 26(5): 1191-1200. (in Chinese)

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