Abstract
Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example, same object with different lighting, background, or pose. Furthermore, in many cases, parts of images can be corrupted by noise or missing entries. In this paper, our goal is to recover images without access to the ground-truth (clean) images using the articulations as structural prior of the data. Such recovery problems fall under the domain of compressive sensing. We propose to learn autoencoder with tensor ring factorization on the the embedding space to impose structural constraints on the data. In particular, we use a tensor ring structure in the bottleneck layer of the autoencoder that utilizes the soft labels of the structured dataset. We empirically demonstrate the effectiveness of the proposed approach for inpainting and denoising applications. The resulting method achieves better reconstruction quality compared to other generative prior-based self-supervised recovery approaches for compressive sensing.
Abstract (translated)
深度网络可以被训练将图像映射到低维度的潜在空间。在许多情况下,一组图像是相互联系的版本;例如,相同的物体以不同的照明、背景或姿态呈现。此外,在许多情况下,图像的部分可以受到噪声或缺失值的影响。在本文中,我们的的目标是使用连接作为数据的结构先验来恢复图像,而连接则作为数据的结构约束。这种恢复问题属于压缩感知技术的范围。我们提议学习使用 Tensor环乘法将嵌入空间中的 Tensor环表示作为编码器的结构先验,并施加数据的结构约束。特别,我们将在编码器的瓶颈层中使用 Tensor环结构,利用结构数据集的软标签。我们经验证了该方法在填充和去噪应用中的的有效性。结果方法相对于其他基于生成先验的自监督恢复方法来说,实现了更好的重建质量。
URL
https://arxiv.org/abs/2303.06235
