Semidecoupled decomposition-based fractional-order variational model for low-light enhancement

Abstract

Low-light enhancement is an important technique for improving image quality. This is because low-light enhancement is expected to improve image visibility while maintaining visual naturalness of the image. In recent years, many methods have been researched to enhance low-light images, including histogram-based, fusion-based, and learning-based methods. The most representative and widely used low-light image enhancement method is the so-called Retinex-based method. However, they tend to have many limitations. The limitations of the Retinex-based method are as follows. (1) Due to strong imaging noise or less-effective image decomposition, this results in a large number of artifacts in the enhanced results. (2) Although the first problem can be partially solved by exploring prior information, it often complicates the optimization process. (3) Small-magnitude details are often lost in enhanced results. To overcome these drawbacks, we propose a model called the fractional-order Retinex model. At the same time, Retinex images are decomposed in an effective semidecoupled way. More concretely, the illumination layer T is gradually estimated only with the observed image S based on the proposed variation model, whereas the reflectance layer R is jointly estimated by the intermediate T and S. Experimental results demonstrate the effectiveness of our method.