Image Restoration: From Sparse and Low-Rank Priors to Deep Priors [Lecture Notes]

Lei Zhang, Wangmeng Zuo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)

Abstract

The use of digital imaging devices, ranging from professional digital cinema cameras to consumer grade smartphone cameras, has become ubiquitous. The acquired image is a degraded observation of the unknown latent image, while the degradation comes from various factors such as noise corruption, camera shake, object motion, resolution limit, hazing, rain streaks, or a combination of them. Image restoration (IR), as a fundamental problem in image processing and low-level vision, aims to reconstruct the latent high-quality image from its degraded observation. Image degradation is, in general, irreversible, and IR is a typical ill-posed inverse problem. Due to the large space of natural image contents, prior information on image structures is crucial to regularize the solution space and produce a good estimation of the latent image. Image prior modeling and learning then are key issues in IR research. This lecture note describes the development of image prior modeling and learning techniques, including sparse representation models, low-rank models, and deep learning models.

Original languageEnglish
Article number8026108
Pages (from-to)172-179
Number of pages8
JournalIEEE Signal Processing Magazine
Volume34
Issue number5
DOIs
Publication statusPublished - 1 Sep 2017

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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