Shir Barzel- SEL-CIE: Knowledge Guided Self Supervised Learning Framework For CIE-XYZ Reconstruction From Non-Linear sRGB Images

Electrical Engineering Systems Zoom Seminar

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https://us04web.zoom.us/j/74301435498?pwd=3KKa8pOfAmAvmY19DtfSeYcW1jJPaZ.1
Meeting ID: 743 0143 5498
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Speaker: Shir Barzel

M.Sc. student under the supervision of Prof. Amir Averbuch and Dr. Ofir Lindenbaum

Wednesday, February 21^st, 2024, at 15:00

SEL-CIE: Knowledge Guided Self Supervised Learning Framework For CIE-XYZ Reconstruction From Non-Linear sRGB Images

Abstract

Modern cameras typically offer two types of image states: a minimally processed linear raw RGB image representing the raw sensor data, and a highly-processed non-linear image state, such as the sRGB state. The CIE-XYZ color space is a device-independent linear space used as part of the camera pipeline and can be helpful for computer vision tasks, such as image deblurring, dehazing, and color recognition tasks in medical applications, where color accuracy is important. However, images are usually saved in non-linear states, and achieving CIE-XYZ color images using conventional methods is not always possible. To tackle this issue, classical methodologies have been developed that focus on reversing the acquisition pipeline. More recently, supervised learning has been employed, using paired CIE-XYZ and sRGB representations of identical images. However, obtaining a large-scale dataset of CIE-XYZ and sRGB pairs can be challenging. To overcome this limitation and mitigate the reliance on large amounts of paired data, self-supervised learning (SSL) can be utilized as a substitute for relying solely on paired data. This work proposes a framework for using SSL methods alongside paired data to reconstruct CIE-XYZ images and re-render sRGB images, outperforming existing approaches.

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