Blind Image Deblurring via Superpixel Segmentation Prior
Bing Luo, Zhongzhe Cheng, Xu Li, Guangrong Zhang, Hongliang Li
Abstract
We present an effective blind image deblurring algorithm based on superpixel segmentation prior (SSP). The motivation of this work is an interesting observation that the more rough the segmentation boundaries are, the clearer the image will be. Intuitively, the blurry images have less image details, which results in more smooth segmentation boundaries. The clear images have more vivid textural details and obtain more rough boundaries. The segmentation roughness could be defined as the length of image segmentation boundaries. However, the segmentation boundary length is not differentiable, making it difficult to integrate into existing joint optimization framework. Therefore, we transform the segmentation boundary length into the segmentation entropy to guide the process of image deblurring. With the image becomes clearer, its boundary becomes more rough, while the segmentation entropy is much smaller. The analysis of relationship between segmentation entropy and segmentation boundaries is detailed. Benefiting from the convexity of segmentation entropy, we propose a novel algorithm by integrating half-quadratic split and gradient descent to alternately minimize energy function. Extensive experiments show that the proposed method achieves best performance with the state-of-the-art blind deblurring methods on natural and face image deblurring.