As a result of several successful applications in computer vision and image processing,
sparse representation (SR) has attracted significant attention in multi-sensor image fusion …

Multi-focus image fusion has emerged as a major topic in image processing to generate all-
focus images with increased depth-of-field from multi-focus photographs. Different …

As a popular signal modeling technique, sparse representation (SR) has achieved great
success in image fusion over the last few years with a number of effective algorithms being …

We present a novel fusion method based on a multi-task robust sparse representation
(MRSR) model and spatial context information to address the fusion of multi-focus gray-level …

Constructing a good dictionary is the key to a successful image fusion technique in sparsity-
based models. An efficient dictionary learning method based on a joint patch clustering is …

In image fusion literature, multi-scale transform (MST) and sparse representation (SR) are
two most widely used signal/image representation theories. This paper presents a general …

Multi-focus image fusion plays an important role in the application of computer vision. In the
process of image fusion, there may be blurring and information loss, so it is our goal to …

In this paper, a novel multi-focus image fusion approach is presented. Firstly, a joint
dictionary is constructed by combining several sub-dictionaries which are adaptively learned …

Multi-modality image fusion is an effective technique to fuse the complementary information
from multi-modality images into an integrated image. The additional information can not only …

In this study, a novel adaptive sparse representation (ASR) model is presented for
simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse …