Blurred Image Region Detection based on Stacked Auto-Encoder

Yuan Zhou, Jianxing Yang, Yang Chen, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this study, we address a fundamental yet challenging problem on detection and classification of blurred regions in partially blurred images. We propose to learn a latent feature representation with stacked auto-encoder (SAE) network to perform blur region detection. Most previous approaches focus on extracting a few blur features in image gradient, Fourier domain, and data-driven local filters. We extract a latent high-level feature representation from such low-level features using the stacked auto-encoder network, thereby improve the accuracy of blur region classification. This high accuracy enables us to successfully separate the clear and blurred regions. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-arts methods in detecting and classifying blur regions in partially blurred images.

Original languageEnglish (US)
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538637883
StatePublished - Nov 26 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other24th International Conference on Pattern Recognition, ICPR 2018

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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