Deep autoencoder-based approaches have achieved significant improvements on restoring damaged images, yet they still suffer from artifacts due to the inadequate representation and inaccurate regularization of existing features. In this paper, we propose a dual-pyramidal inpainting framework called DPNet to address these two limitations, which seamlessly integrates sufficient feature learning and dynamic regularization within an autoencoder network. Specifically, to exhaustively extract multi-scale features, we adopt layer-wise pyramidal convolution in encoder, which provides an arbitrary combination pool of various receptive fields. Subsequently, to tackle the patch deterioration problem in previous cross-scale non-local schemes, we further propose a Pyramidal Attention Mechanism (PAM) in decoder to acquire finer patches directly from learned layers. Mutually benefited with pyramidal features extraction in encoder, the dissemination space for non-local pixels in our PAM is notably enlarged to pyramidal level, thus significantly benefiting the feature representation. Moreover, to avoid the mask error accumulation in existing works, a dynamic normalization mechanism utilizing the spatial mask information updated in encoder is introduced, which further ensures the feature integrity and consistency. Such a dual-pyramidal structure along with dynamic normalization significantly improve the inpainting quality, outperforming existing competitors. Comprehensive experiments conducted on three benchmark datasets demonstrate that our DPNet performs favorably against the state-of-the-arts.

中文翻译：

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动态归一化的双金字塔图像修复

基于深度自动编码器的方法在恢复受损图像方面取得了显着改进，但由于现有特征的表示不足和不准确的正则化，它们仍然存在伪影。在本文中，我们提出了一种称为 DPNet 的双金字塔修复框架来解决这两个限制，它在自动编码器网络中无缝集成了足够的特征学习和动态正则化。具体来说，为了详尽地提取多尺度特征，我们在编码器中采用逐层金字塔卷积，它提供了各种感受野的任意组合池。随后，为了解决先前跨尺度非局部方案中的补丁恶化问题，我们进一步提出了解码器中的金字塔注意机制（PAM），以直接从学习层获取更精细的补丁。与编码器中的金字塔特征提取相得益彰，我们的 PAM 中非局部像素的传播空间显着扩大到金字塔级别，从而显着有利于特征表示。此外，为了避免现有工作中的掩码误差累积，引入了一种利用编码器中更新的空间掩码信息的动态归一化机制，进一步保证了特征的完整性和一致性。这种双金字塔结构以及动态归一化显着提高了修复质量，优于现有竞争对手。