Specular reflection removal using local structural similarity and chromaticity consistency

Zhao, Yongqiang; Peng, Qunnie; Xue, Jize; Kong, Seong G.

doi:10.1109/icip.2015.7351434

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…They then restored saturation based on the respective characteristics of the two reflection components, thereby eliminating highlights. Zhao [23] and colleagues, considering the structural similarity between the reflection component and the highlight image and the local correlation characteristics of chromaticity in the diffuse reflection component, achieved highlight removal by addressing chromaticity joint compensation and local structure. Souza [24] and colleagues, building on previous pixel clustering, analyzed the distribution patterns of chromaticity extremes in the chromaticity space, achieving the goal of pixel clustering.…”

Section: Related Workmentioning

confidence: 99%

Highlight Removal Emphasizing Detail Restoration

Jiang,

Cheng,

Yuan

et al. 2024

Applied Sciences

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In existing highlight removal methods, research on highlights on metal surfaces is relatively limited. Therefore, this paper proposes a new, simple, effective method for removing highlights from metal surfaces, which can better restore image details. Additionally, the approach presented in this paper is highly effective for highlight removal in everyday real-world highlight scenarios. Specifically, we first separate the image’s illumination space based on the Retinex model and generate a highlight mask using the mean plus standard deviation method. Then, based on the mask, we transform the original image and the image at the corresponding mask position to the V channel of the HSV space, achieving the effective elimination of highlights. To enhance the details of the restored image, this paper introduces a method involving adaptive Laplacian sharpening operators and gradient fusion for detail enhancement at highlight removal positions. Finally, a highlight-free image with well-preserved details is obtained. In the experimental phase, we validate the proposed method using real welding seam highlight datasets and real-world highlight datasets. Compared with the existing methods, the proposed method achieves high-quality qualitative and quantitative evaluation.

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Section: Related Workmentioning

confidence: 99%

Highlight Removal Emphasizing Detail Restoration

Jiang,

Cheng,

Yuan

et al. 2024

Applied Sciences

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“…The proposed highlight removal algorithm in Section b ignores the local detail information of the original target and leads to partial color distortion and edge discontinuity after specular highlight removal. So we use our previous work, local chromaticity consistency [46], to realize weighting regularized constraint of highlight removal result and meanwhile variable splitting [47] is combined to achieve fast optimization solving. In this way, the missing information after highlight removal can be effectively compensated in time and the visual effect is further improved.…”

Section: The Compensation Of Missing Information Based On Local Chrom...mentioning

confidence: 99%

A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits

2022

Self Cite

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Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.

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“…Without any constraints, there are numerously possible solutions. Earlier methods propose diferent priors as constraints (e.g., relative smoothness [27], chromaticity consistency [64], ghost efect [47], etc.) to get a meaningful solution.…”

Section: Introductionmentioning

confidence: 99%

Semantic Guided Single Image Reflection Removal

Liu

Li²,

You

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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Reflection is common when we see through a glass window, which not only is a visual disturbance but also influences the performance of computer vision algorithms. Removing the reflection from a single image, however, is highly ill-posed since the color at each pixel needs to be separated into two values belonging to the clear background and the reflection respectively. To solve this, existing methods use additional priors such as reflection layer smoothness, double reflection effect, and color consistency to distinguish the two layers. However, these low-level priors may not be consistently valid in real cases. In this paper, inspired by the fact that human beings can separate the two layers easily by recognizing the objects and understanding the scene, we propose to use the object semantic cue, which is high-level information, as the guidance to help reflection removal. Based on the data analysis, we develop a multi-task end-to-end deep learning method with a semantic guidance component, to solve reflection removal and semantic segmentation jointly. Extensive experiments on different datasets show significant performance gain when using high-level object-oriented information. We also demonstrate the application of our method to other computer vision tasks.

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Specular reflection removal using local structural similarity and chromaticity consistency

Cited by 7 publications

References 18 publications

Highlight Removal Emphasizing Detail Restoration

Highlight Removal Emphasizing Detail Restoration

A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits

Semantic Guided Single Image Reflection Removal

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