Color correction via robust reference selection and recovery using a low-rank matrix model
In this paper, we propose a method that can handle the color correction of a large collection of photographs simultaneously and automatically via robust reference selection. The method does not use any particular model to handle the errors on the photographs, but corrects all kinds of errors caused by changes of viewpoint, large illumination variations, gross pixel corruptions, and partial occlusions under a low-rank matrix model. Furthermore, our method uses the image pixel values directly in vector form, which preserves the spatial information, to obtain the matrix for color correction, unlike other statistics-based image-representation methods such as color histograms. Experiments verify that our method can achieve consistent and promising results on uncontrolled real photographs acquired from the Internet.