Self-learning based image decomposition with applications to single image denoising

De An Huang, Li Wei Kang, Yu Chiang Frank Wang, Chia Wen Lin

Research output: Contribution to journalArticlepeer-review

155 Citations (Scopus)

Abstract

Decomposition of an image into multiple semantic components has been an effective research topic for various image processing applications such as image denoising, enhancement, and inpainting. In this paper, we present a novel self-learning based image decomposition framework. Based on the recent success of sparse representation, the proposed framework first learns an over-complete dictionary from the high spatial frequency parts of the input image for reconstruction purposes. We perform unsupervised clustering on the observed dictionary atoms (and their corresponding reconstructed image versions) via affinity propagation, which allows us to identify image-dependent components with similar context information. While applying the proposed method for the applications of image denoising, we are able to automatically determine the undesirable patterns (e.g., rain streaks or Gaussian noise) from the derived image components directly from the input image, so that the task of single-image denoising can be addressed. Different from prior image processing works with sparse representation, our method does not need to collect training image data in advance, nor do we assume image priors such as the relationship between input and output image dictionaries. We conduct experiments on two denoising problems: single-image denoising with Gaussian noise and rain removal. Our empirical results confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art image denoising algorithms.

Original languageEnglish
Article number6623207
Pages (from-to)83-93
Number of pages11
JournalIEEE Transactions on Multimedia
Volume16
Issue number1
DOIs
Publication statusPublished - 2014 Jan
Externally publishedYes

Keywords

  • Denoising
  • image decomposition
  • rain removal
  • self-learning
  • sparse representation

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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