Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion
Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion
复制标题
用于位深度扩展的最低有效位的深度重构
DOI：
10.1109/tip.2019.2891131
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发表时间：
2019
影响因子：
10.6
通讯作者：
Wen Gao
中科院分区：
文献类型：
--
作者：
Yang Zhao;Ronggang Wang;Wei Jia;Wangmeng Zuo;Xiaoping Liu;Wen Gao
位深度扩展(BDE)对于在高位深监视器中显示低位深图像非常重要。目前的BDE算法往往使用传统的方法来填充缺失的最低有效位，并且受到多种可感知伪影的影响。在本文中，我们提出了一种基于深度残差网络的BDE方法。根据平坦和非平坦区域的不同特性，提出了两种不同的通道来分别重建这两种区域。此外，在平坦区域信道中，提出了一种简单而有效的局部自适应平差预处理方法。该方法结合了传统去带策略和基于网络的重建方法的优点，可以进一步提高平坦区域的主观质量。在多个图像集上的实验结果表明，所提出的BDE网络能够获得良好的视觉质量和较好的量化性能。
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 and decent quantitative performance.