Achieving structured low-rank representation from the original image is a challenging and significant task, owing to the capacity of the low-rank structure in expressing structured information from the real world. It is noteworthy that, most of the existing methods to obtain the low-rank textures, treat this issue as a “transformational problem”, which lead to the poor quality of the images with complex backgrounds. In order to jump out of this interference, we try to explore this issue as a “generative problem” and propose the Low-rank texture Generative Adversarial Network (LR-GAN) using an unsupervised image-to-image network. Our method generates the high-quality low-rank texture gradually from the low-rank constraint after many iterations of training. Considering that the low-rank constraint is difficult to optimize (NP-hard problem) in the loss function, we introduce the layer of the low-rank gradient filter to approach the optimal low-rank solution. Experimental results demonstrate that the proposed method is effective on both synthetic and real world images.
CITATION STYLE
Zhao, S., & Li, J. (2017). Generating Low-Rank Textures via Generative Adversarial Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10636 LNCS, pp. 310–318). Springer Verlag. https://doi.org/10.1007/978-3-319-70090-8_32
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