Highlight detection and removal from spectral image

Koirala, Pesal; Pant, Paras; Hauta-Kasari, Markku; Parkkinen, Jussi

doi:10.1364/josaa.28.002284

Cited by 13 publications

(12 citation statements)

References 10 publications

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“…Some work [17][18] [19] require robust color segmentation for accurate specular detection, which is quite challenging. Tan and Ikeuchi [20] make use of the difference between specular and diffuse pixels in their proposed specular-free image to remove the highlight effects.…”

Section: A Related Workmentioning

confidence: 99%

Fast and High Quality Highlight Removal From a Single Image

Suo

et al. 2016

IEEE Trans. on Image Process.

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Abstract-Specular reflection exists widely in photography and causes the recorded color deviating from its true value, so fast and high quality highlight removal from a single nature image is of great importance. In spite of the progress in the past decades in highlight removal, achieving wide applicability to the large diversity of nature scenes is quite challenging. To handle this problem, we propose an analytic solution to highlight removal based on an L2 chromaticity definition and corresponding dichromatic model. Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively. In the former illumination orthogonal subspace, which is specular-free, we can conduct robust clustering with an explicit criterion to determine the cluster number adaptively. In the latter illumination parallel subspace, a property called pure diffuse pixels distribution rule (PDDR) helps map each specular-influenced pixel to its diffuse component. In terms of efficiency, the proposed approach involves few complex calculation, and thus can remove highlight from high resolution images fast. Experiments show that this method is of superior performance in various challenging cases.Index Terms-Specular reflection, highlight removal, specular and diffuse, L2 normalized dichromatic model, adaptive material clustering.

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Section: A Related Workmentioning

confidence: 99%

Fast and High Quality Highlight Removal From a Single Image

Suo

et al. 2016

IEEE Trans. on Image Process.

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“…In general, all of pre-processing routines have already been described and employed by many authors for different applications (ElMasry and Nakauchi 2016;Nguyen-Do-Trong et al, 2019), but very few studies (e.g. Fu, Tan et al 2006;Koirala, Pant et al 2011;Zheng, Sato et al 2015) were devoted towards dealing with specular problems encountered in hyperspectral images, especially those ones that will be used for real-time applications in food processing plants. In fact, the implementation of hyperspectral imaging systems at the industrial level is not straightforward, as several issues must be addressed such as noise reduction and removal of specularity highlight problems (Sivertsen, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

ElMasry

Gou

Al-Rejaie

2021

Journal of Food Engineering

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Specularity or highlight problem exists widely in hyperspectral images, provokes reflectance deviation from its true value, and can hide major defects in food objects or detecting spurious false defects causing failure of inspection and detection processes. In this study, a new non-iterative method based on the dichromatic reflection model and principle component analysis (PCA) was proposed to detect and remove specular highlight components from hyperspectral images acquired by various imaging modes and under different configurations for numerous agro-food products. To demonstrate the effectiveness of this approach, the details of the proposed method were described and the experimental results on various spectral images were presented. The results revealed that the method worked well on all hyperspectral and multispectral images examined in this study, effectively reduced the specularity and significantly improves the quality of the extracted spectral data. Besides the spectral images from available databases, the robustness of this approach was further validated with real captured hyperspectral images of different food materials. By using qualitative and quantitative evaluation based on running time and peak signal to noise ratio (PSNR), the experimental results showed that the proposed method outperforms other specularity removal methods over the datasets of hyperspectral and multispectral images.

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“…The decomposition was then used for skin recognition, material clustering, and specularity removal. Koirala et al (2011) have another approach. They detect and remove specularity with a filter that coefficients are found by constrained energy minimization.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic RGB and multispectral images recovery by independent quadratic programming

Krebs¹,

Benezeth²,

Marzani³

2020

PeerJ Computer Science

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This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation, or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the state of the art in this domain, our approach has the advantage to address a complex inverse problem into two parallelizable optimization steps that are easy to solve and do not require learning. The proposed method is an extension of a previous algorithm that is rewritten to be numerically more stable, has better quantitative and qualitative results, and applies to multispectral images. The proposed method is assessed qualitatively and quantitatively on standard RGB and multispectral datasets.

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Highlight detection and removal from spectral image

Cited by 13 publications

References 10 publications

Fast and High Quality Highlight Removal From a Single Image

Fast and High Quality Highlight Removal From a Single Image

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

Intrinsic RGB and multispectral images recovery by independent quadratic programming

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