WB LUTS: CONTRASTIVE LEARNING FOR WHITE BALANCING LOOKUP TABLES

作     者：Manne, Sai Kumar Reddy Wan, Michael 

作者机构：Roux Institute Northeastern University United States Institute for Experiential AI Northeastern University United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Table lookup 

摘      要：Automatic white balancing (AWB), one of the first steps in an integrated signal processing (ISP) pipeline, aims to correct the color cast induced by the scene illuminant. An incorrect white balance (WB) setting or AWB failure can lead to an undesired blue or red tint in the rendered sRGB image. To address this, recent methods pose the post-capture WB correction problem as an image-to-image translation task and train deep neural networks to learn the necessary color adjustments at a lower resolution. These low resolution outputs are post-processed to generate high resolution WB corrected images, forming a bottleneck in the end-to-end run time. In this paper we present a 3D Lookup Table (LUT) based WB correction model called WB LUTs that can generate high resolution outputs in real time. We introduce a contrastive learning framework with a novel hard sample mining strategy, which improves the WB correction quality of baseline 3D LUTs by 25.5%. Experimental results demonstrate that the proposed WB LUTs perform competitively against state-of-the-art models on two benchmark datasets while being 300× faster using 12.7× less memory. Our model and code are available at https://***/skrmanne/3DLUT_sRGB_WB1 © 2024, CC BY.