Recently, deep dictionary learning (DDL) has aroused attention due to its abilities of
learning multiple different dictionaries and extracting multi-level abstract feature …

With the great success of deep neural networks, combining deep learning with traditional
dictionary learning has become a hot issue. However, the performance of these methods is …

Deep dictionary learning (DDL) shows good performance in visual classification tasks.
However, almost all existing DDL methods ignore the locality relationships between the …

In order to fast sample an image and accurately reconstruct the image from a small amount
of sampled data, we design a novel deep network for optimization-based algorithm mapping …

Deep dictionary learning (DDL) can mine deeper representations of data more effectively
than single-layer dictionary learning. However, existing DDL methods with specific sparse …

Nonlinear dictionary learning (NLDL) can mine nonlinear information in data better than
linear models. Mainstream NLDL methods are based on kernel methods; however, their …

The identification of low-resolution fingerprints has always been one of the focuses in the
field of biometric identification. This paper proposes a method for super-resolving low …

The method of image super-resolution reconstruction through the dictionary usually only
uses a single-layer dictionary, which not only cannot extract the deep features of the image …

In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical
fiber intrusion signal recognition based on SCN (TSVD‐SCN) based on truncated singular …

The method of image super-resolution reconstruction through a dictionary usually only uses
a single-layer dictionary, which not only fails to extract the deep features of the image, but …