Training-Based Color Correction for Camera Phone Images

In this paper, we propose a method for improving the color rendition of low quality cell phone camera images. The proposed method is based on a multi layer stochastic framework whose parameters are learned in an offline training procedure using the well known expectation maximization (EM) algorithm. The color correction algorithm functions by first making soft assignments of images into defect classes and then processing images in each defect class with an optimized algorithm, which we refer to as resolution synthesis-based color correction (RSCC). The parameters of the color correction algorithm are trained using pairs of low quality images, obtained from real cell phone cameras, and high quality spatially registered reference images, captured with a high quality digital still camera. We present experimental results comparing the performance of our method to some existing commercial color correction algorithms.