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A complexity reduction based retinex model for low luminance retinal fundus image enhancement

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Abstract

Retinal fundus images play significant roles in the early detection and treatment of various ocular diseases. However, they are often suffered from low luminance in the process of shooting. To address this problem, we propose a Complexity Reduction Retinex (CR\(^2\)) model for the enhancement of low luminance retinal fundus images. The proposed method enables the divided illumination component to be spatially smooth and the reflectance component to be piece-wise continuous. Meanwhile, to improve the computational efficiency, we divide the illumination and reflection components into two independent sub-problems and solve them efficiently by Alternating Direction Minimizing (ADM) method. Comparative results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of qualitative and quantitative evaluations.

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References

  • Cai B, Xu X, Guo K, Jia K, Hu B, Tao D (2017)A joint intrinsic-extrinsic prior model for retinex. In: Proceedings of the International Conference on Computer Vision (ICCV), pp 4020–4029

  • Çelik T, Tjahjadi T (2011) Contextual and variational contrast enhancement. IEEE Trans Image Process 20:3431–3441

    Article  MathSciNet  MATH  Google Scholar 

  • Dong X, Wang G, Pang Y, Li W, Wen J, Meng W, Lu Y (2011) Fast efficient algorithm for enhancement of low lighting video. In: 2011 IEEE International Conference on Multimedia and Expo, IEEE, pp 1–6

  • Elad M (2005) Retinex by two bilateral filters. International conference on scale-space theories in computer vision. Springer, Cham, pp 217–229

    Google Scholar 

  • Fu X, Liao Y, Zeng D, Huang Y, Zhang X, Ding X (2015) A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans Image Process 24:4965–4977

    Article  MathSciNet  MATH  Google Scholar 

  • Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J (2016) A fusion-based enhancing method for weakly illuminated images. Signal Process 129:82–96

    Article  Google Scholar 

  • Gao Y, Hu HM, Li B, Guo Q (2018) Naturalness preserved nonuniform illumination estimation for image enhancement based on retinex. IEEE Trans Multimed 20:335–344

    Article  Google Scholar 

  • González R (1981) Woods R Digital image processing. IEEE Trans Pattern Anal Mach Intell 3:242–243

    Google Scholar 

  • Guo C, Li C, Guo J, Loy CC, Hou J, Kwong S, Cong R (2020) Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1780–1789

  • Gu Z, Li F, Fang F, Zhang G (2020) A novel retinex-based fractional-order variational model for images with severely low light. IEEE Trans Image Process 29:3239–3253

    Article  MathSciNet  MATH  Google Scholar 

  • Guo X, Li Y, Ling H (2017) Lime: low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26:982–993

    Article  MathSciNet  MATH  Google Scholar 

  • Hong M, Luo ZQ (2017) On the linear convergence of the alternating direction method of multipliers. Math Progr 162(1):165–199

    Article  MathSciNet  MATH  Google Scholar 

  • Huynh-Thu Q, Ghanbari M (2008) Scope of validity of psnr in image/video quality assessment. Electron Lett 44(13):800–801

    Article  Google Scholar 

  • Ibrahim H (2007) Kong N Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electr 53:1752–1758

    Article  Google Scholar 

  • Jeon G, Jung MY, Anisetti M, Bellandi V, Damiani E, Jeong J (2010) Specification of the geometric regularity model for fuzzy if-then rule-based deinterlacing. J Disp Technol 6(6):235–243

    Article  Google Scholar 

  • Jiang Y, Gong X, Liu D, Cheng Y, Fang C, Shen X, Yang J, Zhou P, Wang Z (2021a) Enlightengan: deep light enhancement without paired supervision. IEEE Trans Image Process 30:2340–2349

    Article  Google Scholar 

  • Jiang Z, Li H, Liu L, Men A, Wang H (2021b) A switched view of retinex: deep self-regularized low-light image enhancement. Neurocomputing 454:361–372

    Article  Google Scholar 

  • Jobson D, Rahman Z, Woodell G (1997a) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6(3):451–462. https://doi.org/10.1109/83.557356

    Article  Google Scholar 

  • Jobson DJ, Rahman Z, Woodell GA (1997b) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965–76

    Article  Google Scholar 

  • Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Article  Google Scholar 

  • Kimmel R, Elad M, Shaked D, Keshet R, Sobel I (2004) A variational framework for retinex. Int J Comput Vis 52:7–23

    Article  MATH  Google Scholar 

  • Lai Z, Chen L, Jeon G, Liu Z, Zhong R, Yang X (2021) Real-time and effective pan-sharpening for remote sensing using multi-scale fusion network. J Real-Time Image Proc 18(5):1635–1651

    Article  Google Scholar 

  • Land E (1977) The retinex theory of color vision. Sci Am 237(6):108–28

    Article  Google Scholar 

  • Lee CH, Shih JL, Lien C, Han CC (2013) Adaptive multiscale retinex for image contrast enhancement. In: 2013 International Conference on Signal-Image Technology Internet-Based Systems, pp 43–50

  • Li B, Peng Z, Hou P, He M, Anisetti M, Jeon G (2019) Reliability and capability based computation offloading strategy for vehicular ad hoc clouds. J Cloud Comput 8(1):1–14

    Article  Google Scholar 

  • Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. In Proceedings of the 24th international conference on neural information processing systems (NIPS'11), pp 612–620

  • Lore KG, Akintayo A, Sarkar S (2017) Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662

    Article  Google Scholar 

  • Mittal A, Soundararajan R, Bovik A (2013) Making a completely blind image quality analyzer. IEEE Signal Process Lett 20:209–212

    Article  Google Scholar 

  • Pang J, Zhang S, Bai WA (2017) Novel framework for enhancement of the low lighting video. IEEE Symp Comput Commun (ISCC). https://doi.org/10.1109/ISCC.2017.8024714

    Article  Google Scholar 

  • Pizer SM (1990) Contrast-limited adaptive histogram equalization: Speed and effectiveness stephen m. pizer, r. eugene johnston, james p. ericksen, bonnie c. yankaskas, keith e. muller medical image display research group. In: Proceedings of the First Conference on Visualization in Biomedical Computing, Atlanta, Georgia, vol. 337

  • Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research. Data 3(3):25

    Article  Google Scholar 

  • Rahman Z, Jobson DJ, Woodell GA (2004) Retinex processing for automatic image enhancement. J. Electron Imaging 13:100–110

    Article  Google Scholar 

  • Ren X, Yang W, Cheng WH, Liu J (2020) Lr3m: robust low-light enhancement via low-rank regularized retinex model. IEEE Trans Image Process 29:5862–5876

    Article  MathSciNet  MATH  Google Scholar 

  • Singh RP, Dixit M (2015) Histogram equalization: a strong technique for image enhancement. Int J Signal Process Image Process Pattern Recognit 8(8):345–352

    Google Scholar 

  • Tao L, Zhu C, Xiang G, Li Y, Jia H, Xie X (2017) Llcnn: a convolutional neural network for low-light image enhancement. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp 1–4

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  • Wang S, Zheng J, Hu H, Li B (2013) Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process 22(9):3538–3548

    Article  Google Scholar 

  • Wang J, Anisetti M, Jeon G (2019) Reconstruction of missing color-channel data using a three-step back propagation neural network. Int J Mach Learn Cybern 10(10):2631–2642

    Article  Google Scholar 

  • Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement. In: BMVC

  • Wu J, Anisetti M, Wu W, Damiani E, Jeon G (2016) Bayer demosaicking with polynomial interpolation. IEEE Trans Image Process 25(11):5369–5382

    Article  MathSciNet  MATH  Google Scholar 

  • Wu Y, Song W, Zheng J, Liu F (2020) Noisy low-light image enhancement using reflectance similarity prior. IEEE Int Conf Signal Process (ICSP) 1:160–164. https://doi.org/10.1109/ICSP48669.2020.9321010

    Article  Google Scholar 

  • Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6):1

    Google Scholar 

  • Xu J, Yu M, Liu L, Zhu F, Shao L (2020) Star: a structure and texture aware retinex model. IEEE Trans Image Process 29:5022–5037

    Article  MATH  Google Scholar 

  • Yang W, Wang S, Fang Y, Wang Y, Liu J (2020) From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3063–3072

  • Zhang J, Dashtbozorg B, Bekkers EJ, Pluim JPW, Duits R, ter Haar Romeny BM (2016) Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans Med Imaging 35:2631–2644

    Article  Google Scholar 

  • Zhang Y, Zhang J, Guo X (2019) Kindling the darkness: a practical low-light image enhancer. In: inproceedings of the 27th ACM International Conference on Multimedia

  • Zhang Y, Di X, Zhang B, Wang C (2020) Self-supervised image enhancement network: training with low light images only. arXiv preprint arXiv:2002.11300

  • Zuiderveld KJ (1994) Contrast limited adaptive histogram equalization. In: Graphics Gems

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61801272) and National Natural Science Foundation of Shandong Province (Nos.ZR2021QD041 and ZR2020MF127).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, China

    Xuesong Li, Mingliang Gao, Jianrun Shang & Jinfeng Pan

  2. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK

    Qilei Li

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  1. Xuesong Li
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  2. Mingliang Gao
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Correspondence to Mingliang Gao.

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Li, X., Gao, M., Shang, J. et al. A complexity reduction based retinex model for low luminance retinal fundus image enhancement. Netw Model Anal Health Inform Bioinforma 11, 30 (2022). https://doi.org/10.1007/s13721-022-00373-3

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