By integrating the information contained in multiple images of the same scene into one
composite image, pixel-level image fusion is recognized as having high significance in a variety …

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 …

As is well known, activity level measurement and fusion rule are two crucial factors in image
fusion. For most existing fusion methods, either in spatial domain or in a transform domain …

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 …

Multi-focus image fusion technique is an important approach to obtain a composite image
with all objects in focus. The key point of multi-focus image fusion is to develop an effective …

The fusion of infrared and visible images of the same scene aims to generate a composite
image which can provide a more comprehensive description of the scene. In this paper, we …

In real world, an image is usually associated with multiple labels which are characterized by
different regions in the image. Thus image classification is naturally posed as both a multi-…

Both unmanned vehicles and driver assistance systems require solving the problem of
traffic sign recognition. A lot of work has been done in this area, but no approach has been …

Underwater images often suffer from color distortion and visibility degradation due to the
light absorption and scattering. Existing methods utilize various assumptions/constrains to …

Conventional graph-based semi-supervised learning methods predominantly focus on single
label problem. However, it is more popular in real-world applications that an example is …