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Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion.

Abstract

Bit-depth expansion (BDE) is important for displaying a low bit-depth image in a high bit-depth monitor. Current BDE algorithms often utilize traditional methods to fill the missing least significant bits and suffer from multiple kinds of perceivable artifacts. In this paper, we present a deep residual network-based method for BDE. Based on the different properties of flat and non-flat areas, two channels are proposed to reconstruct these two kinds of areas, respectively. Moreover, a simple yet efficient local adaptive adjustment preprocessing is presented in the flat-area-channel. By combining the benefits of both the traditional debanding strategy and network-based reconstruction, the proposed method can further promote the subjective quality of the flat area. Experimental results on several image sets demonstrate that the proposed BDE network can obtain favorable visual quality as well as decent quantitative performance.

Authors

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Pub Type(s)

Journal Article

Language

eng

PubMed ID

30624217

Citation

Zhao, Yang, et al. "Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion." IEEE Transactions On Image Processing : a Publication of the IEEE Signal Processing Society, 2019.
Zhao Y, Wang R, Jia W, et al. Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion. IEEE Trans Image Process. 2019.
Zhao, Y., Wang, R., Jia, W., Zuo, W., Liu, X., & Gao, W. (2019). Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion. IEEE Transactions On Image Processing : a Publication of the IEEE Signal Processing Society, doi:10.1109/TIP.2019.2891131.
Zhao Y, et al. Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion. IEEE Trans Image Process. 2019 Jan 7; PubMed PMID: 30624217.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion. AU - Zhao,Yang, AU - Wang,Ronggang, AU - Jia,Wei, AU - Zuo,Wangmeng, AU - Liu,Xiaoping, AU - Gao,Wen, Y1 - 2019/01/07/ PY - 2019/1/10/entrez JF - IEEE transactions on image processing : a publication of the IEEE Signal Processing Society JO - IEEE Trans Image Process N2 - Bit-depth expansion (BDE) is important for displaying a low bit-depth image in a high bit-depth monitor. Current BDE algorithms often utilize traditional methods to fill the missing least significant bits and suffer from multiple kinds of perceivable artifacts. In this paper, we present a deep residual network-based method for BDE. Based on the different properties of flat and non-flat areas, two channels are proposed to reconstruct these two kinds of areas, respectively. Moreover, a simple yet efficient local adaptive adjustment preprocessing is presented in the flat-area-channel. By combining the benefits of both the traditional debanding strategy and network-based reconstruction, the proposed method can further promote the subjective quality of the flat area. Experimental results on several image sets demonstrate that the proposed BDE network can obtain favorable visual quality as well as decent quantitative performance. SN - 1941-0042 UR - https://www.unboundmedicine.com/medline/citation/30624217/Deep_Reconstruction_of_Least_Significant_Bits_for_Bit-Depth_Expansion L2 - https://dx.doi.org/10.1109/TIP.2019.2891131 DB - PRIME DP - Unbound Medicine ER -