Retinex based reflectance decomposition using convolutional sparse coding

Color constancy is a feature of the human visual system, which detects the relative inconsistency of the perceived color of an object so that the perceived color may not be changed under various illumination conditions, even when the conditions for observing color are changed. Thus, the Retinex theory was designed in consideration of this color constancy. The physics based Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of the object However, if there are many detail areas in the image or the illumination changes rapidly, the illumination and reflectance may not be decomposed properly, because of the violation of the smoothness constraint on illumination. In this paper, we use the convolutional sparse coding model to represent the reflectance in more detail. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in experimental results. Consequently, we can decompose Retinex based illumination and reflectance more precisely, then, reduce the perception gap between humans and machines.