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In this paper, we propose a hierarchical color correction algorithm for enhancing the color of digital images obtained from low-quality digital image capture devices such as cell phone cameras. The proposed method is based on a multilayer hierarchical stochastic framework whose parameters are learned in an offline training procedure using the well-known expectation maximization (EM) algorithm. This hierarchical framework functions by first making soft assignments of images into defect classes and then processing the images in each defect class with an optimized algorithm. The hierarchical color correction is performed in three stages. In the first stage, global color attributes of the low-quality input image are used in a Gaussian mixture model (GMM) framework to perform a soft classification of the image into M predefined global image classes. In the second stage, the input image is processed with a nonlinear color correction algorithm that is designed for each of the M global classes. This color correction algorithm, which we refer to as resolution synthesis color correction (RSCC), applies a spatially varying color correction determined by the local color attributes of the input image. In the third stage, the outputs of the RSCC predictors are combined using the global classification weights to yield the color corrected output image. We compare the performance of the proposed method to other commercial color correction algorithms on cell phone camera images obtained from different sources. Both subjective and objective measures of quality indicate that the new color correction algorithm improves quality over the existing methods.