Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries
Jun-Jie Huang, Pier Luigi Dragotti

TL;DR
This paper introduces DeepCAM, a deep convolutional analysis dictionary model that uses convolutional dictionaries for high-dimensional signals, demonstrating competitive performance in image super-resolution tasks.
Contribution
It proposes a novel multilayer convolutional analysis dictionary learning approach, extending previous models with convolutional dictionaries for improved efficiency and effectiveness.
Findings
Achieves performance comparable to state-of-the-art methods in super-resolution.
Demonstrates good generalization capabilities across tasks.
Introduces an efficient learning algorithm for convolutional analysis dictionaries.
Abstract
In this paper, we introduce a Deep Convolutional Analysis Dictionary Model (DeepCAM) by learning convolutional dictionaries instead of unstructured dictionaries as in the case of deep analysis dictionary model introduced in the companion paper. Convolutional dictionaries are more suitable for processing high-dimensional signals like for example images and have only a small number of free parameters. By exploiting the properties of a convolutional dictionary, we present an efficient convolutional analysis dictionary learning approach. A L-layer DeepCAM consists of L layers of convolutional analysis dictionary and element-wise soft-thresholding pairs and a single layer of convolutional synthesis dictionary. Similar to DeepAM, each convolutional analysis dictionary is composed of a convolutional Information Preserving Analysis Dictionary (IPAD) and a convolutional Clustering Analysis…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Image Processing Techniques and Applications
