Endoscopic image enhancement with noise suppression

Stereoscopic endoscopes have been used increasingly in minimally invasive surgery to visualise the organ surface and manipulate various surgical tools. However, insufficient and irregular light sources become major challenges for endoscopic surgery. Not only do these conditions hinder image processing algorithms, sometimes surgical tools are barely visible when operating within low-light regions. In addition, low-light regions have low signal-to-noise ratio and metrication artefacts due to quantisation errors. As a result, present image enhancement methods usually suffer from heavy noise amplification in low-light regions. In this Letter, the authors propose an effective method for endoscopic image enhancement by identifying different illumination regions and designing the enhancement design criteria for desired image quality. Compared with existing image enhancement methods, the proposed method is able to enhance the low-light region while preventing noise amplification during image enhancement process. The proposed method is tested with 200 images acquired by endoscopic surgeries. Computed results show that the proposed algorithm can outperform state-of-the-art algorithms for image enhancement, in terms of naturalness image quality evaluator and illumination index.

[1]  Hany Farid,et al.  Blind inverse gamma correction , 2001, IEEE Trans. Image Process..

[2]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

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

[4]  E. Land The retinex theory of color vision. , 1977, Scientific American.

[5]  Zia-ur Rahman,et al.  Multi-scale retinex for color image enhancement , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[6]  Heinrich Niemann,et al.  A System for Real-Time Endoscopic Image Enhancement , 2003, MICCAI.

[7]  Cheolkon Jung,et al.  Perceptual Enhancement of Low Light Images Based on Two-Step Noise Suppression , 2018, IEEE Access.

[8]  Gang Wang,et al.  Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree , 2014, IEEE Transactions on Image Processing.

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

[10]  Rafal Mantiuk,et al.  Real-time noise-aware tone mapping , 2015, ACM Trans. Graph..

[11]  Yu Li,et al.  LIME: Low-Light Image Enhancement via Illumination Map Estimation , 2017, IEEE Transactions on Image Processing.

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

[13]  Lilong Shi,et al.  The Rehabilitation of MaxRGB , 2010, CIC.

[14]  Mark S. Drew,et al.  The Role of Bright Pixels in Illumination Estimation , 2012, Color Imaging Conference.

[15]  David A. Forsyth,et al.  A Novel Approach To Colour Constancy , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[16]  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..