  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
{\gamma}) of an observed image to generate its structure map and texture map.
The structure map is produced by been amplified with {\gamma} > 1, while the
texture map is generated by been shrank with {\gamma} < 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 {\gamma}. 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.
