A Smoke Removal Method for Laparoscopic Images

In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces error for the image processing (used in image guided surgery), but also reduces the visibility of the surgeons. In this paper, we propose to enhance the laparoscopic images by decomposing them into unwanted smoke part and enhanced part using a variational approach. The proposed method relies on the observation that smoke has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained unwanted smoke component is then subtracted from the original degraded image, resulting in the enhanced image. The obtained quantitative scores in terms of FADE, JNBM and RE metrics show that our proposed method performs rather well. Furthermore, the qualitative visual inspection of the results show that it removes smoke effectively from the laparoscopic images.

[1]  Codruta O. Ancuti,et al.  Single Image Dehazing by Multi-Scale Fusion , 2013, IEEE Transactions on Image Processing.

[2]  Suyash P. Awate,et al.  Joint desmoking and denoising of laparoscopy images , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[3]  Danail Stoyanov,et al.  Surgical Vision , 2011, Annals of Biomedical Engineering.

[4]  Ling Shao,et al.  A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior , 2015, IEEE Transactions on Image Processing.

[5]  Qing Liu,et al.  Fast image dehazing using improved dark channel prior , 2012, 2012 IEEE International Conference on Information Science and Technology.

[6]  Ho Chan Numerical optimization for image and video restoration , 2011 .

[7]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[8]  Guang-Zhong Yang,et al.  Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery , 2017, ArXiv.

[9]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[10]  Guang-Zhong Yang,et al.  Probabilistic Tracking of Affine-Invariant Anisotropic Regions , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Javier Vazquez-Corral,et al.  Enhanced Variational Image Dehazing , 2015, SIAM J. Imaging Sci..

[12]  Danail Stoyanov,et al.  Chromaticity based smoke removal in endoscopic images , 2017, Medical Imaging.

[13]  Terry M. Peters,et al.  Vision-Based Surgical Field Defogging , 2017, IEEE Transactions on Medical Imaging.

[14]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[15]  Jean-Philippe Tarel,et al.  BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES , 2011 .

[16]  Jean-Philippe Tarel,et al.  Fast visibility restoration from a single color or gray level image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Alan Conrad Bovik,et al.  Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging , 2015, IEEE Transactions on Image Processing.

[18]  Danail Stoyanov,et al.  Chromaticity based Smoke Removal in Endoscopic Image , 2017 .

[19]  Javier Vazquez-Corral,et al.  A Variational Framework for Single Image Dehazing , 2014, ECCV Workshops.

[20]  Javier Vazquez-Corral,et al.  Fusion-Based Variational Image Dehazing , 2017, IEEE Signal Processing Letters.

[21]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Faouzi Alaya Cheikh,et al.  An adaptive contrast enhancement method for stereo endoscopic images combining binocular just noticeable difference model and depth information , 2016, IQSP.

[23]  Truong Q. Nguyen,et al.  An Augmented Lagrangian Method for Total Variation Video Restoration , 2011, IEEE Transactions on Image Processing.

[24]  S. N. Merchant,et al.  Joint desmoking, specularity removal, and denoising of laparoscopy images via graphical models and Bayesian inference , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[25]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.