An automatic framework for endoscopic image restoration and enhancement
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An automatic framework for endoscopic image restoration and enhancement Muhammad Asif1
· Lei Chen1 · Hong Song1 · Jian Yang2 · Alejandro F. Frangi3
Accepted: 1 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Despite its success in the field of minimally invasive surgery, endoscopy image analysis remains challenging due to limited image settings and control conditions. The low resolution and existence of large number of reflections in endoscopy images are the major problems in the automatic detection of objects. To address these issues, we presented a novel framework based on the convolutional neural networks. The proposed approach consists of three major parts. First, a deep learning (DL)-based image evaluation method is used to classify the input images into two groups, namely, specular highlights and weakly illuminated groups. Second, the specular highlight is detected using the DL-based method, and the reflected areas are recovered through a patch-based restoration operation. Lastly, gamma correction with optimized reflectance and illumination estimation is adopted to enhance the weakly illuminated images. The proposed method is compared against the existing ones, and the experimental results demonstrate that the former outperforms the latter in terms of subjective and objective assessments. This finding indicates that the proposed approach can serve as a potential tool for improving the quality of the endoscopy images used to examine internal body organs. Keywords Endoscopy · Image restoration · Image enhancement · Specular highlights · Weak illuminance
1 Introduction The development of endoscopy image processing technology has received increasing attention due to the widespread use of minimally invasive treatments [1]. These computer vision-based technologies provide an observable endoscopy view of the internal organs to help physicians make highly accurate diagnosis [2] Despite the great progress of natural image processing techniques, such as image restoration and enhancement, only few methods can be applied to
Hong Song
[email protected] Jian Yang
[email protected] 1
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
2
School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
3
School of Computing and School of Medicine, University of Leeds, Leeds, UK
endoscopy scenes due to the unique acquisition processes and imaging environment. Two common situations affect the quality of endoscopy images. One is the bright spots produced by the light reflections in the smooth organs’ surface (Fig. 1). These spots, which are caused by specular reflections, can result in the loss of image texture and color information and leads to significant discontinuities in endoscopy imaging and affects the physician’s vision, which are not conducive to diagnosis tasks [3, 4]. The automatic detection and restoration of specular reflections are popular processes, and many researchers promote this stream by prop
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