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Intrinsic RGB and multispectral images recovery by independent quadratic programming.

ABSTRACT: This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation, or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the state of the art in this domain, our approach has the advantage to address a complex inverse problem into two parallelizable optimization steps that are easy to solve and do not require learning. The proposed method is an extension of a previous algorithm that is rewritten to be numerically more stable, has better quantitative and qualitative results, and applies to multispectral images. The proposed method is assessed qualitatively and quantitatively on standard RGB and multispectral datasets.

SUBMITTER: Krebs A

PROVIDER: S-EPMC7924503 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Intrinsic RGB and multispectral images recovery by independent quadratic programming.

Krebs Alexandre A Benezeth Yannick Y Marzani Franck F

PeerJ. Computer science 20200210

This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation, or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the st ...[more]

PMID: 33816908

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