A Method of Removing Reflected Highlight on Images Based on Polarimetric Imaging

Abstract

A method of removing reflected highlight is proposed on polarimetric imaging. Polarization images (0 ∘ , 45 ∘ , 90 ∘ , and 135 ∘ ) and the reflection angle are required in this reflected light removal algorithm. This method is based on the physical model of reflection and refraction, and no additional image processing algorithm is necessary in this algorithm. Compared to traditional polarization method with single polarizer, restricted observation angle of Brewster is not demanded and multiple reflection areas of different polarization orientations can be removed simultaneously. Experimental results, respectively, demonstrate the features of this reflected light removal algorithm, and it can be considered very suitable in polarization remote sensing.

Effectiveness of specularity removal from hyperspectral images on the quality of spectral signatures of food products

“…Numerous experimental studies have been performed to obtain insights into specularity phenomenon and to develop models that explain the mechanisms of separating diffuse and specular components (Lehmann and Palm 2001;Tan and Ikeuchi 2008;Artusi, Banterle et al 2011;Yamamoto, Kitajima et al 2017). In colour images, detection of specular and diffuse pixels has been investigated intensively (Shen, Zhang et al 2008;Shen and Zheng 2013;Nguyen, Vo et al 2014;Suo, An et al 2016;Yang, Tang et al 2016) by using either single image techniques or multiple images-based techniques. In the single-image techniques, the specularity problem is detected or removed based either on colour space transformation or on spatial information analysis (Shen and Zheng 2013;Akashi and Okatani 2016;Yamamoto, Kitajima et al 2017;Guo, Zhou et al 2018).…”

“…When a bundle of light rays hits a food sample, two types of reflections namely specular and diffuse reflections are generated (Shen, Zhang et al 2008;Tan and Ikeuchi 2008;Yang, Tang et al 2016;Guo, Zhou et al 2018). Specular reflection is a mirror-like reflection of light from a sample causing the problem of specularity or highlights (Tan and Ikeuchi 2008;Yang, Tang et al 2015).…”

“…In computer vision and hyperspectral imaging applications, the specularity problem may be ignored completely, treated as outliers, reduced or removed, or alternatively used it as a source of information (Tan and Ikeuchi 2008;Hamid 2013). The simplest way to avoid specularity problem and its influences is by using cross setting of a polarizing filter in front of the camera sensor and light source during image acquisition (Nayar, Fang et al 1997;Yoon, Choi et al 2006; Koirala, Pant et al 2011;Yang, Tang et al 2016;Nguyen-Do-Trong et al, 2019). Alternatively, it could be solved by using a low-pass filter to the maximum fraction of the chromaticity to smooth out the variance due to specular pixels in the image.…”

Polarization mapping

“…Polarimetric information has been used to study different and diverse fields. In computer vision, for instance, polarimetric images have been applied to eliminate the haze of an image [5] or specular highlights [6,7] using polarization. With a different goal (general photographic editing), our approach is able to eliminate haze or highlights as a consequence of luminance minimization, while providing several other editing operators.…”

A simple distance measurement instrument based on the law of light reflection

“…The existence of measuring instruments makes physicists able to conduct investigations into natural phenomena that occur in everyday life [3]. In addition, innovative measuring P a P e r…”