SRPS–deep-learning-based photometric stereo using superresolution images. Issue 4 (14th June 2021)

• Record Type: Journal Article
• Authors: Song, Euijeong; Kim, Seokjung; Chung, Seok; Chang, Minho
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 4(2021)
• Page Start: 995
• Page End: 1012
• Publication Date: 2021-06-14
• Subjects: computer vision -- photometric stereo -- deep learning -- convolutional neural network -- image superresolution; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab025
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: This paper introduces a novel deep-learning-based photometric stereo method that uses superresolution (SR) images: SR photometric stereo. Recent deep-learning-based SR algorithms have yielded great results in terms of enlarging images without mosaic effects. Supposing that the SR algorithms successfully enhance the feature and colour information of original images, implementing SR images using the photometric stereo method facilitates the use of considerably more information on the object than existing photometric stereo methods. We built a novel deep-learning-based network for the photometric stereo technique to optimize the input–output of SR image inputs and normal map outputs. We tested our network using the most widely used benchmark dataset and obtained better results than existing photometric stereo methods. Graphical Abstract: