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Research Article | Open Access

Discriminative feature encoding for intrinsic image decomposition

Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beihang University, Beijing 100191, China
Peng Cheng Laboratory, Shenzhen 518000, China
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Graphical Abstract

Abstract

Intrinsic image decomposition is an important and long-standing computer vision problem. Given an input image, recovering the physical scene properties is ill-posed. Several physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image decomposition. This work takes advantage of deep learning, and shows that it can solve this challenging computer vision problem with high efficiency. The focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input image. To achieve this goal, we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding space. We define feature distribution divergence to efficiently separate the feature vectors of different intrinsic components. The feature distributions are also constrained to fit the real ones through a feature distribution consistency. In addition, a data refinement approach is provided to remove data inconsistency from the Sintel dataset, making it more suitable for intrinsic image decomposition. Our method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent frames. Experimental results indicate that our proposed network structure can outperform the existing state-of-the-art.

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Computational Visual Media
Pages 597-618
Cite this article:
Wang Z, Liu Y, Lu F. Discriminative feature encoding for intrinsic image decomposition. Computational Visual Media, 2023, 9(3): 597-618. https://doi.org/10.1007/s41095-022-0294-4

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Received: 11 January 2022
Accepted: 09 May 2022
Published: 18 April 2023
© The Author(s) 2023.

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