Detail-richest-channel based enhancement for retinal image and beyond

Abstract The quality of retinal photography is not always satisfactory due to the limited imaging conditions. Low contrast and insufficient brightness are common problems. By comprehensively analyzing the image formation model from an intuitive perspective, we present general principles of parameter settings for different enhancement scenarios, and this analysis provides theoretical support for designing the proposed method. In a retinal image, the green channel preserves the richest details due to the red free photography. We term the green channel as the “detail-richest-channel” in a retinal image, and it is used as the initial transmission map. Subsequently, the transmission map is corrected by the modified cubic function to reduce over enhancement. The proposed method is designed for enhancing retinal images photographed by handheld fundus camera initially, and it can be extended to other applications. A total of 1045 retinal images photographed by handheld fundus camera, 460 retinal images acquired from cataract patients using high-end fundus camera, and 890 underwater images are tested to validate the effectiveness of the proposed method. Results show that the proposed method can improve the quality of degraded images as well as preserve the natural visual perception.

[1]  Dacheng Tao,et al.  An Underwater Image Enhancement Benchmark Dataset and Beyond , 2019, IEEE Transactions on Image Processing.

[2]  Xi Chen,et al.  A novel approach of edge detection based on gray weighted absolute correlation degree and Prewitt operator , 2010, 2010 International Conference on Intelligent Computing and Integrated Systems.

[3]  Xiaochun Cao,et al.  Single Image Dehazing via Multi-scale Convolutional Neural Networks , 2016, ECCV.

[4]  Li Xiong,et al.  An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis , 2017, Journal of healthcare engineering.

[5]  Codruta O. Ancuti,et al.  Enhancing underwater images and videos by fusion , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jia Zhang,et al.  A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection , 2014, Comput. Medical Imaging Graph..

[7]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[9]  Chen Gao,et al.  Human-Visual-System-Inspired Underwater Image Quality Measures , 2016, IEEE Journal of Oceanic Engineering.

[10]  Sudipta Roy,et al.  Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of cataract , 2018, Comput. Methods Programs Biomed..

[11]  Li Cheng,et al.  Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis , 2019, IEEE Transactions on Medical Imaging.

[12]  Mario Fernando Montenegro Campos,et al.  Underwater Depth Estimation and Image Restoration Based on Single Images , 2016, IEEE Computer Graphics and Applications.

[13]  Sumohana S. Channappayya,et al.  Blind image quality evaluation using perception based features , 2015, 2015 Twenty First National Conference on Communications (NCC).

[14]  Adrian Galdran,et al.  Automatic Red-Channel underwater image restoration , 2015, J. Vis. Commun. Image Represent..

[15]  Pamela C. Cosman,et al.  Generalization of the Dark Channel Prior for Single Image Restoration , 2018, IEEE Transactions on Image Processing.

[16]  Bhupendra Gupta,et al.  Color retinal image enhancement using luminosity and quantile based contrast enhancement , 2019, Multidimens. Syst. Signal Process..

[17]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[18]  Chong-Yi Li,et al.  Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior. , 2016, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[19]  Huiqi Li,et al.  An enhancement method for color retinal images based on image formation model , 2017, Comput. Methods Programs Biomed..

[20]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[21]  Runmin Cong,et al.  A hybrid method for underwater image correction , 2017, Pattern Recognit. Lett..

[22]  László G. Nyúl,et al.  Glaucoma risk index:  Automated glaucoma detection from color fundus images , 2010, Medical Image Anal..

[23]  Shuhang Wang,et al.  Naturalness Preserved Image Enhancement Using a priori Multi-Layer Lightness Statistics. , 2018, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[24]  Xinghao Ding,et al.  Two-step approach for single underwater image enhancement , 2017, 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[25]  Mei Zhou,et al.  Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment , 2018, IEEE Transactions on Biomedical Engineering.

[26]  Xiao-Ping Zhang,et al.  A retinex-based enhancing approach for single underwater image , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[27]  Alexei A. Efros,et al.  Contrastive Learning for Unpaired Image-to-Image Translation , 2020, ECCV.

[28]  Pamela C. Cosman,et al.  Underwater Image Restoration Based on Image Blurriness and Light Absorption , 2017, IEEE Transactions on Image Processing.

[29]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[30]  Luís Alberto da Silva Cruz,et al.  Retinal image quality assessment using generic image quality indicators , 2014, Inf. Fusion.

[31]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[32]  Jianxin Zhang,et al.  Edge Detection Based on General Grey Correlation and LoG Operator , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[33]  Jianqiang Li,et al.  Exploiting ensemble learning for automatic cataract detection and grading , 2016, Comput. Methods Programs Biomed..

[34]  Hai-Miao Hu,et al.  Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images , 2013, IEEE Transactions on Image Processing.

[35]  Jaskirat Kaur,et al.  A generalized method for the segmentation of exudates from pathological retinal fundus images , 2018 .

[36]  Arcot Sowmya,et al.  An Underwater Color Image Quality Evaluation Metric , 2015, IEEE Transactions on Image Processing.

[37]  Pradeep Venkatesh,et al.  Detection of retinal lesions in diabetic retinopathy: comparative evaluation of 7-field digital color photography versus red-free photography , 2012, International Ophthalmology.