Intrinsic Image Decomposition Based on Retinex Theory, Superpixel Segmentation and Scale-Space Computations

Intrinsic image decomposition enables us to estimate the low-level features of images. Due to the benefits it provides and the challenges it holds, intrinsic image decomposition has been extensively studied over more than five decades. It can be utilized in various computer vision and computer graphics pipelines to improve the efficiency of tasks such as object classification and recoloring, and image segmentation. In this study, we introduce an algorithm for reflectance and shading estimation, offering a simple yet effective solution to the ill-posed intrinsic image decomposition problem. Our learning-free method leverages a combination of the fundamentals of the Retinex theory, scale-space computations, and superpixel segmentation. The assumptions of the Retinex theory enable us to provide a straightforward solution to a complex problem, while scale-space computations allow us to highlight low-level features and superpixel segmentation helps us to preserve local information. We evaluated our algorithm that mainly focuses on single objects on three benchmarks, namely, MIT Intrinsic Images, Bonneel, and MPI Sintel, which either consist of single objects or complex scenes having different characteristics. According to our experiments, our algorithm provides competitive results compared to the other methods.