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Image Error Concealment Based on Deep Neural Network

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College of Information Science and Technology, Donghua University, Shanghai 201620, China
2
Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Shanghai 201620, China
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Authors to whom correspondence should be addressed.
Algorithms 2019, 12(4), 82; https://doi.org/10.3390/a12040082
Received: 4 March 2019 / Revised: 3 April 2019 / Accepted: 10 April 2019 / Published: 19 April 2019
In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics. View Full-Text
Keywords: image error concealment; deep neural network; local correlation and non-local self-similarity; adaptive scan order image error concealment; deep neural network; local correlation and non-local self-similarity; adaptive scan order
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Zhang, Z.; Huang, R.; Han, F.; Wang, Z. Image Error Concealment Based on Deep Neural Network. Algorithms 2019, 12, 82.

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