3 research outputs found
Learning Optimization-inspired Image Propagation with Control Mechanisms and Architecture Augmentations for Low-level Vision
In recent years, building deep learning models from optimization perspectives
has becoming a promising direction for solving low-level vision problems. The
main idea of most existing approaches is to straightforwardly combine numerical
iterations with manually designed network architectures to generate image
propagations for specific kinds of optimization models. However, these
heuristic learning models often lack mechanisms to control the propagation and
rely on architecture engineering heavily. To mitigate the above issues, this
paper proposes a unified optimization-inspired deep image propagation framework
to aggregate Generative, Discriminative and Corrective (GDC for short)
principles for a variety of low-level vision tasks. Specifically, we first
formulate low-level vision tasks using a generic optimization objective and
construct our fundamental propagative modules from three different viewpoints,
i.e., the solution could be obtained/learned 1) in generative manner; 2) based
on discriminative metric, and 3) with domain knowledge correction. By designing
control mechanisms to guide image propagations, we then obtain convergence
guarantees of GDC for both fully- and partially-defined optimization
formulations. Furthermore, we introduce two architecture augmentation
strategies (i.e., normalization and automatic search) to respectively enhance
the propagation stability and task/data-adaption ability. Extensive experiments
on different low-level vision applications demonstrate the effectiveness and
flexibility of GDC.Comment: 15 page