Institute of Information Science, Academia Sinica

Abstract: In this talk, I will present some recent results on image enhancements by exploiting structural and sparsity information. In the first part, I will discuss a motion deblurring algorithm that exploits sparsity constraints of image patches using one single frame. In our formulation, each image patch is encoded with sparse coefficients using an over-complete dictionary. The sparsity constraints facilitate recovering the latent image without solving an ill-posed deconvolution problem. The dictionary is learned and updated directly from one single frame without using additional images. The proposed method iteratively utilizes sparsity constraints to recover latent image, estimates the deblur kernel, and updates the dictionary directly from one single image. The final deblurred image is then recovered once the deblur kernel is estimated using our method. In addition, I will present some preliminary results on finding good features for this task. In the second part, I will describe a super-resolution method that exploits self-similarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Furthermore, class-specific mapping can be learned to generate high-quality images with rich texture details and sharp edges without user assistance. --------------------------------------------------------- Bio: Ming-Hsuan Yang is an assistant professor in EECS at University of California, Merced. He received the PhD degree in computer science from University of Illinois at Urbana-Champaign in 2000. Prior to joining UC Merced, he was a senior research scientist at Honda Research Institute working on vision problems related to humanoid robots. In 1999, he received the Ray Ozzie fellowship for his research work. He coauthored the book Face Detection and Gesture Recognition for Human-Computer Interaction (Kluwer Academic 2001) and is one of the guest editors for a special issue on face recognition for Computer Vision and Image Understanding, 2003 as well as a special issue on real-world face recognition for IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011. He serves in different capacities for a few conferences (area chair for ICCV 2011, AAAI 2011, FG 2011, ACCV 2010, CVPR 2009, ACCV 2009, CVPR 2008, and publication chair for CVPR 2010). He is an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence, and Image and Vision Computing.