Image Filtering 2.0: Efficient Edge-Aware Filtering and Their Applications

Jiangbo Lu (ADSC), Dongbo Min (ADSC), Minh N. Do (UIUC)


Description: The last decade has witnessed the exciting development of efficient edge-aware filtering (EAF) techniques, stemming from different theories and principles. Thanks to their strong power in adaptively dealing with various visual signals as well as significant computational and implementation advantages, this kind of modern image filtering techniques have found a great variety of applications in image/video processing, computer vision and computer graphics. These applications range broadly from addressing the classical image denoising task to recently tackling fully connected high-order conditional random field (CRFs) for accurate semantic image segmentation and labeling. In these applications, the EAF methods have been employed either to allow for local data adaptivity or to efficiently integrate global supports. In this tutorial, we will first present various state-of-the-art nonlinear EAF techniques, while revealing theoretical connections, new insights and generalization. Especially, we will focus on fast filtering approaches, including using the bilateral grid, color-line model, multipoint aggregation, domain transform, and recursive data propagation. Representative applications of these filtering techniques will be illustrated and discussed, including edge-preserving smoothing, color image denoising, stereo matching, depth map enhancement, stylization, detail enhancement, and image segmentation/matting. We will also cover recent research works that efficiently deal with a fully-connected or high-order Conditional Random Field (CRF) model using the efficient EAF algorithms (e.g. based on mean field approximation or quadratic programming).
  After spending a large part of the tutorial on efficient filtering and applications, we will discuss a serious computational challenge faced by most cost volume filtering-based approaches (and also MRF-based approaches), i.e., the curse of the huge discrete space in labeling problems (e.g. dense stereo and optical flow estimation). Along this direction, we will present some recent works to tackle such research challenges e.g. using a randomized hypothesis sampling and propagation approach (e.g. PatchMatch) or importance sampling on the label space, and then introduce several issues that should be addressed for developing more powerful computational tools.
  Finally, we will conclude this tutorial by suggesting some intriguing problems and exciting future directions that may drive this adaptive filtering paradigm further into a cost-effective, general-purpose tool for many applications, going beyond the recent attempts of high-order CRFs inference or the randomized search approaches.
 
Contact
Dr. Dongbo Min: dongbo (at) adsc (dot) com (dot) sg,  dbmin99 (at) gmail (dot) com
Dr. Jiangbo Lu: jiangbo (dot) lu (at) adsc (dot) com (dot) sg
 
 
Course Material
  • Part 1: Introduction (ppt, rar)
  • Part 2: Efficient edge-aware filter techniques (ppt, rar)
  • Part 3: Image processing and graphics applications (ppt, rar)
  • Part 4: Dense discrete labeling problems for vision tasks (ppt, rar)
   If you can't download the 'ppt' files due to some weird problems of 'Dropbox' (automatically converting 'ppt' files to 'pdf' format), please download the 'rar' files.
 
 
Code
          (Note: More codes will be available, once they are ready.)
 
 
Bibliography
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