Non-uniform illumination endoscopic imaging enhancement via anti-degraded model and L 1 L 2-based variational retinex
© The Author(s). 2017
Received: 26 September 2017
Accepted: 15 November 2017
Published: 4 December 2017
In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L 1 L 2-based variational retinex (AD-L 1 L 2VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between the detail component of the haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using the proposed L 1 L 2-based variational retinex (L 1 L 2VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods.
Nowadays, signal processing [1–3] and image processing [4–6] get more and more attention. Amongst them, medical image processing has been widely researched. Diseases of the gastrointestinal tract, such as bleeding, ulcer, and tumor, are threatening humans’ health. However, traditional diagnosis methods, such as barium meal examination, X-ray scanning, and CT, are invasive to the human body. After the invention of the endoscope, it is possible to generate color images directly inside the human body. In 2000, capsule endoscopy (CE)  was introduced, and it has been a useful tool for examining the entire gastrointestinal tract, especially for the screening of small-bowel diseases [5, 6]. However, endoscopic diagnosis is time consuming due to the great amount of the video data and low contrast image quality. Besides, the misdiagnosis rate may increase because of blurred edges and low contrast of images.
With the purpose of improving diagnostic detection rate, several techniques or devices have been proposed to optimize visualization . Add-on devices , wide-angle colonoscopies [9–12], and balloon colonoscope  are the examples of advanced imaging devices which have been widely used to improve diagnostic yield. Color enhancement technique at the chip level or as a post-processing step is another method to increase the image quality and diagnostic yield . The Fuji Intelligent Color Enhancement (FICE, Fujinon Inc.) system , narrow-band imaging (NBI) , I-scan , and retinex  are the examples of post-processing color enhancement algorithms which have been widely used [6, 19].
In order to diagnose disease successfully, we make the following three major contributions on color image enhancement technologies. First, preserving naturalness as much as possible because color is one of the most important bases for diagnosing pathology. Second, preventing the scattering caused by mucosa and digestive juice inside the human body. Third, more and more details should be displayed for improving the diagnostic rate.
Amongst the various enhancement methods, retinex has received much attention due to its simplicity and effectiveness in enhancing non-uniform illumination images . To simulate the mechanism of HVS, it is an ill-posed problem that computes illumination or reflectance from a single observed image. In order to get more accurate results, many modified retinex methods have been proposed. Path-based retinex  methods are the simplest, but they usually necessitate high computational complexity. Jobson et al. had proposed the multi-scale retinex (MSR) [22, 23] algorithm and the color restored multi-scale retinex (CRMSR)  algorithm. Partial differential equation (PDE) was introduced to the retinex algorithm in 1974 . However, when solving the Poisson equations, extra artifacts will be caused by the hard thresholding operator in PDE-based retinex algorithms. In 2011, a total variational retinex method (TVR)  was proposed. In 2014, a variational Bayesian model for retinex was proposed by Wang et al. .
However, the issue of atmospheric transmission is important but not considered in existing classical enhancement methods. In real endoscopic imaging scenes, images captured by endoscope will be influenced by the scattering and absorption of mucosa and digestive juice inside the human body. In order to overcome this drawback, a novel endoscopic imaging enhancement via anti-degraded model and L 1 L 2-based variational retinex (AD-L 1 L 2VR) is proposed in this paper. Before enhancing an observed image, an anti-degraded model named dark channel prior (DCP) is provided to get haze-free endoscopic images. Secondly, the haze-free endoscopic image will be decomposed into detail and naturalness components by light filtering, and these two parts will be discussed separately. Then, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. In addition, most retinex methods assume that the estimated error between observed image and the product of reflectance and background illumination is a random variable with a Gaussian distribution with zero mean and variance δ 2. The maximum likelihood estimation (MLE) solution of Gaussian distribution is equivalent to the solution of ordinary least squares (OLS). However, the OLS solution is sensitive to outliers although it is easy to solve. If the error is Laplacian distributed, the MLE solution is equivalent to the least absolute deviation (LAD) solution. Compared with the OLS method, the LAD method is robust to outliers. So, we assume that the error between the detail component of the haze-free image and the product of the associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component will be obtained by using the proposed L 1 L 2-based variational retinex (L 1 L 2VR) model. Finally, the recombination of associated reflectance and naturalness component become the final result.
This paper is organized as follows: the optical model and the retinex model are described in Section 2. Section 3 gives the details of the proposed algorithm and the optimization strategy. Experimental results and evaluation are shown in Section 4. Discussion is shown in Section 5. Section 6 concludes the paper.
2.1 Optical model
2.2 Retinex model
where s = log(S), l = log(L), and r = log(R).
3.1 Anti-degraded model
3.2 Image decomposition based on light filtering
After decomposition, the detail and naturalness components can be processed separately. The mapped naturalness component can be acquired by using LLGC. And reflectance can be obtained by the processing detail component via L 1 L 2VR.
3.3 Naturalness mapping using LLGC
3.4 Image decomposition via L 1 L 2VR
Computing illumination or reflectance from a single observed image is ill-posed. To solve this problem, many variational retinex models have been proposed. In this paper, the reflectance component is acquired via a L 1 L 2VR model with simultaneously estimating illumination and reflectance.
Likelihood p(S|R, L)
Compared with the OLS method, the LAD method is robust to outliers .In this paper, we assume the error vector follows an additive combination of two independent distributions: Gaussian and Laplacian distributions.
3.5 Split Bregman algorithm for the proposed model
3.6 Synthesis reflectance and naturalness
5.1 Subjective assessment
5.2 Objective assessment
Table 1 shows the quantitative measurement results of the contrast. As shown in Table 1, LHE and MSR have higher contrast than the other methods. However, the proposed algorithm and the NPEA method have a better subjective assessment performance than the other three methods.
The third metric is LOE, which is used to evaluate naturalness preservation. According to the definition of LOE, a smaller LOE value means representing better naturalness preservation. As shown Table 3, the proposed algorithm has the best naturalness preservation performance.
In summary, compared with other relevant state-of-the-art enhancement methods, the proposed algorithm not only preserve more details and prevent halo artifacts, but also prevent color cast caused by scattering. The proposed algorithm can achieve good quality from both subjective and objective assessments. It is a good way to increase diagnosis and reduce misdiagnosis for endoscopic imaging.
This paper proposes a novel image enhancement algorithm via anti-degraded model and L 1 L 2-based variational retinex theory (AD-L 1 L 2VR) for non-uniform illumination endoscopic images, which not only enhances the details of the image but also preserves the naturalness. The anti-degraded model is used to prevent color cast caused by scattering. In order to estimate the reflectance and background illumination component, L 1 L 2VR is proposed to constrain the TV regularization strength. Moreover, logarithmic Laplacian-based gamma correction is conducted on the naturalness component for preventing color cast caused by non-uniform illumination or scattering. Experimental results demonstrate that the proposed algorithm has a better performance than the other existing algorithms.
The authors would like to thank Image Engineering &Video Technology Lab for the support.
This work was supported by the Major Science Instrument Program of the National Natural Science Foundation of China under Grant 61527802, the General Program of National Nature Science Foundation of China under Grants 61371132 and 61471043, and the International S&T Cooperation Program of China under Grant 2014DFR10960.
Availability of data and materials
All data are fully available without restriction.
ZR and TX came up with the algorithm and improved the algorithm. In addition, ZR wrote and revised the paper. JL, JG, and GS implemented the algorithm of LHE, MSR, and SARV for image enhancement, and HW recorded the data. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- F Zhao, H Nie, H Chen, Group buying spectrum auction algorithm for fractional frequency reuses cognitive cellular systems. Ad Hoc Netw. 58, 239–246 (2017)View ArticleGoogle Scholar
- F Zhao, W Wang, H Chen, Q Zhang. “Interference alignment and game-theoretic power allocation in MIMO Heterogeneous Sensor Networks communications,” Signal Processing. 2016;126(C):173–9.Google Scholar
- F Zhao, L Wei, H Chen, Optimal time allocation for wireless information and power transfer in wireless powered communication systems. IEEE Trans. Veh. Technol. 65(3), 1830–1835 (2016)Google Scholar
- G Iddan, G Merson, A Glukhovsky, P Swain, Wireless capsule endoscopy. Nature 405, 417–418 (2000)View ArticleGoogle Scholar
- DK Iakovidis, A Koulaouzidis, Software for enhanced video capsule endoscopy: challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3), 172–186 (2015)View ArticleGoogle Scholar
- A Koulaouzidis, E Rondonotti, A Karargyris, Small-bowel capsule endoscopy: a ten-point contemporary review. World J. Gastroenterol. 19(24), 3726–3746 (2013)View ArticleGoogle Scholar
- T Matsuda, A Ono, M Sekiguchi, et al., Advances in image enhancement in colonoscopy for detection of adenomas. Nat. Rev. Gastroenterol. Hepatol. 14, 305–314 (2017)View ArticleGoogle Scholar
- SC Ng et al., The efficacy of cap-assisted colonoscopy in polyp detection and cecal intubation: a meta-analysis of randomized controlled trials. Am. J. Gastroenterol. 107, 1165–1173 (2012)View ArticleGoogle Scholar
- VP Deenadayalu, V Chadalawada, DK Rex, 170 degrees wide-angle colonoscope: effect on efficiency and miss rates. Am. J. Gastroenterol. 99, 2138–2142 (2004)View ArticleGoogle Scholar
- H Fatima et al., Wide-angle (WA) (170° angle of view) versus standard (ST) (140°angle of view) colonoscopy [abstract]. Gastrointest. Endosc. 63, AB204 (2013)Google Scholar
- T Uraoka et al., A novel extra-wide-angle-view colonoscope: a simulated pilot study using anatomic colorectal models. Gastrointest. Endosc. 77, 480–483 (2013)View ArticleGoogle Scholar
- IM Gralnek et al., Comparison of standard forward-viewing mode versus ultrawide-viewing mode of a novel colonoscopy platform: a prospective, multicenter study in the detection of simulated polyps in an in vitro colon model (with video). Gastrointest. Endosc. 77, 472–479 (2013)View ArticleGoogle Scholar
- N Hasan et al., A novel balloon colonoscope detects significantly more simulated polyps than a standard colonoscope in a colon model. Gastrointest. Endosc. 80, 1135–1140 (2014)View ArticleGoogle Scholar
- F Deeba, SK Mohammed, FM Bui, et al., Unsupervised abnormality detection using saliency and Retinex based color enhancement. Conf Proc IEEE Eng Med Biol Soc 2016, 3871–3874 (2016)Google Scholar
- Y Miyake, T Kouzu, et al., Development of new electronic endoscopes using the spectral images of an internal organ. 13th Color Imaging Conf Final Program Proc 13(3), 261–263 (2005)Google Scholar
- Y Hamamoto, T Endo, et al., Usefulness of narrow-band imaging endoscopy for diagnosis of Barrett’s esophagus. J. Gastroenterol. 39(1), 14–20 (2004)View ArticleGoogle Scholar
- A Hoffman et al., Recognition and characterization of small colonic neoplasia with high-definition colonoscopy using i-Scan is as precise as chromoendoscopy. Dig. Liver Dis. 42(1), 45–50 (2010)View ArticleGoogle Scholar
- H Okuhata et al., Application of the real-time Retinex image enhancement for endoscopic images. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2013, 3407–3410 (2013)Google Scholar
- S Rajput, SR Suralkar, Comparative study of image enhancement techniques. Int J Comput Sci Mobile Comput 2(1), 11 (2013)Google Scholar
- L Wang, L Xiao, H Liu, Z Wei, Variational Bayesian method for retinex. IEEE Trans. Image Process. 23(8), 3381–3396 (2014)MathSciNetView ArticleMATHGoogle Scholar
- G Gianini, A Rizzi, E Damiani, A Retinex model based on absorbing Markov chains. Inf. Sci. 327(C), 149–174 (2016)MathSciNetView ArticleGoogle Scholar
- Z Rahman, DJ Jobson, GA Woodell, Multi-scale retinex for color image enhancement. in Proc. ICIP 3, 1003–1006 (1996)Google Scholar
- ZU Rahman, DJ Jobson, GA Woodell, Retinex processing for automatic image enhancement. J. Electron. Imag. 13(1), 100–110 (2004)View ArticleGoogle Scholar
- ZU Rahman, DJ Jobson, GA Woodell, Investigating the relationship between image enhancement and image compression in the context of the multi-scale retinex. J Vis Commun Image Representation 22(3), 237–250 (2011)View ArticleGoogle Scholar
- K Horn, Determining lightness from an image. Comput Graph Image Process (4), 277–299 (1974)Google Scholar
- MK Ng, W Wang, A total variation model for retinex. SIAM J. Imag. Sci. 4(1), 345–365 (2011)MathSciNetView ArticleMATHGoogle Scholar
- E Land, J Mccann, Lightness and retinex theory. J. Opt. Soc. Am. 61(61), 1–11 (1971)View ArticleGoogle Scholar
- K He, J Sun, X Tang, Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)View ArticleGoogle Scholar
- K He, J Sun, X Tang, Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)View ArticleGoogle Scholar
- Wang D, Lu H, Yang M H. “Least soft-threshold squares tracking,” Proceedings of the IEEE conference on computer vision and pattern recognition. 2013;9(4):2371–8.Google Scholar
- D Zosso, G Tran, SJ Osher, Non-local Retinex—a unifying framework and beyond[J]. Siam J Imaging Sci 8(2), 787–826 (2015)MathSciNetView ArticleMATHGoogle Scholar
- R Kimmel, M Elad, D Shaked, R Keshet, I Sobel, A variational framework for Retinex. Int. J. Comput. Vis. 52(1), 7–23 (2003)View ArticleMATHGoogle Scholar
- T Goldstein, S Osher, The split Bregman algorithm for L1 regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)MathSciNetView ArticleMATHGoogle Scholar
- TK Kim, JK Paik, BS Kang, Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consum. Electron. 44(1), 82–87 (1998)View ArticleGoogle Scholar
- X Lan, H Shen, L Zhang, A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images. Signal Process. 101(8), 19–34 (2014)View ArticleGoogle Scholar
- S Wang, J Zheng, HM Hu, Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)View ArticleGoogle Scholar
- E Peli, Contrast in complex images. J Opt Soc Am A Opt Image Sci 7(10), 2032–2032 (1990)View ArticleGoogle Scholar
- Z. Ye, H. Mohamadian, and Y. Ye, “Discrete entropy and relative entropy study on nonlinear clustering of underwater and arial images,” in Proc. IEEE Int. Conf. Control Appl., pp. 318–323 (2007)Google Scholar