In this study, we address a fundamental yet challenging problem on detection and classification of blurred regions in partially blurred images. We propose to learn a latent feature representation with stacked auto-encoder (SAE) network to perform blur region detection. Most previous approaches focus on extracting a few blur features in image gradient, Fourier domain, and data-driven local filters. We extract a latent high-level feature representation from such low-level features using the stacked auto-encoder network, thereby improve the accuracy of blur region classification. This high accuracy enables us to successfully separate the clear and blurred regions. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-arts methods in detecting and classifying blur regions in partially blurred images.