STAR: A Structure and Texture Aware Retinex Model

Authors

Jun Xu, Nankai University
Yingkun Hou, Taishan University
Dongwei Ren, Tianjin University
Li Liu, Inception Institute of Artificial Intelligence
Fan Zhu, Inception Institute of Artificial Intelligence
Mengyang Yu, Inception Institute of Artificial Intelligence
Haoqian Wang, Tsinghua University
Ling Shao, Inception Institute of Artificial Intelligence & Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Image Processing

Abstract

Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent γ ) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with γ > 1, while the texture map is generated by been shrank with γ < 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at https://github.com/csjunxu/STAR.

First Page

5022

Last Page

5037

DOI

10.1109/TIP.2020.2974060

Publication Date

3-11-2020

Keywords

color correction, low-light image enhancement, Retinex decomposition

Comments

IR Deposit conditions:

OA version (pathway a) Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Recommended Citation

J. Xu et al., "STAR: A Structure and Texture Aware Retinex Model," in IEEE Transactions on Image Processing, vol. 29, pp. 5022-5037, 2020, doi: 10.1109/TIP.2020.2974060.

Additional Links

DOI link: https://doi.org/10.1109/TIP.2020.2974060