Blur image identification with ensemble convolution neural networks

Blur image classification is a key step to image recovery in image processing. In this article, an ensemble convolution neural network (CNN) is designed to identify and classify four types of blur images: defocus blur, Gaussian blur, haze blur, and motion blur. To achieve this, a two-stage pipeline, comprised of deep compression and ensemble technique, is proposed to enhance model discriminability without incurring additional computing burden. Specifically, our method first prunes the well-known networks, Alexnet and GoogleNet, by an appropriate compression ratio. The pruned networks are denoted as Simplified-Fast-Alexnet (SFA) and Simplified-Fast-GoogleNet (SFGN). Next, we employ an ensemble policy to integrate the SFA with SFGN as SFA+SFGN by assigning their respective weights based on a voting mechanism. In addition, to provide a benchmark set of blur image samples for training and testing blur classification models, we create a new public blur image dataset (available online at http://doip.buaa.edu.cn/info/1092/1073.htm) containing 80,000+ patch-level, naturally blurred photographs, constructed using the improved super-pixel segmentation method, and 200,000+ artificially blurred images. Numerical experiments demonstrate the superior performance of the proposed approach in comparison with the original Alexnet and GoogleNet, as well as other state-of-the-art methods.