Enhanced residual dense intrinsic network for intrinsic image decomposition
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- 论文类型：会议论文
- 发表时间：2019-07-08
- 收录刊物：EI
- 卷号：2019-July
- 页面范围：1462-1467
- 摘要：Intrinsic image decomposition is a challenging task, which aims at recovering intrinsic components from the observation. Hand-crafted priors have been widely used in traditional methods, yet with unsatisfactory performance of quality and runtime. Recently, network-based approaches have been greatly developed, but the physical imaging principle is ignored causing the multiplication of estimated components is hard to reconstruct the observation. To overcome these limitations, we develop an enhanced residual dense intrinsic network (ERDIN) for intrinsic decomposition. Specifically, we construct the basic module (i.e., enhanced residual dense block (ERDB)) to fully exploit the hierarchical features. The physical imaging principle is designed as the reconstruction loss to ensure the consistency between the observation and the multiplication of estimated components, which is of equal importance with the data loss. Extensive experimental results illustrate our excellent performance compared with other state-of-the-art methods. ? 2019 IEEE.
- 发表时间：2019-07-08
