Preprint Dataset Version 1 Preserved in Portico This version is not peer-reviewed

S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images

Sangho Jo
ORCID logo ,
Ohtae Jang
,
Chaitali Bhattacharyya
,
Minjun Kim
,
Taeseok Lee
,
Yewon Jang
,
Haekang Song
,
Hyeokmin Kwon
,
Saebyeol Do
,
Sungho Kim
* ORCID logo
Version 1 : Received: 16 November 2023 / Approved: 17 November 2023 / Online: 21 November 2023 (10:00:38 CET)

How to cite: Jo, S.; Jang, O.; Bhattacharyya, C.; Kim, M.; Lee, T.; Jang, Y.; Song, H.; Kwon, H.; Do, S.; Kim, S. S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images. Preprints 2023, 2023111183. https://doi.org/10.20944/preprints202311.1183.v1 Jo, S.; Jang, O.; Bhattacharyya, C.; Kim, M.; Lee, T.; Jang, Y.; Song, H.; Kwon, H.; Do, S.; Kim, S. S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images. Preprints 2023, 2023111183. https://doi.org/10.20944/preprints202311.1183.v1

Abstract

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a Specular-Light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the Multi-Scale Normalized Cross Correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

Keywords

Single Image based Deep Learning Model, Specular Highlight Removal, Reflection Removal, Synthetic Dataset, Multi-Scale Normalized Cross Correlation (MS-NCC)

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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