  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. Unlike the traditional multiscale transforms (MSTs)
that presume the basis functions, SR learns an over-complete dictionary from a
set of training images for image fusion, and it achieves more stable and
meaningful representations of the source images. By doing so, the SR-based
fusion methods generally outperform the traditional MST-based image fusion
methods in both subjective and objective tests. In addition, they are less
susceptible to mis-registration among the source images, thus facilitating the
practical applications. This survey paper proposes a systematic review of the
SR-based multi-sensor image fusion literature, highlighting the pros and cons
of each category of approaches. Specifically, we start by performing a
theoretical investigation of the entire system from three key algorithmic
aspects, (1) sparse representation models; (2) dictionary learning methods; and
(3) activity levels and fusion rules. Subsequently, we show how the existing
works address these scientific problems and design the appropriate fusion rules
for each application, such as multi-focus image fusion and multi-modality
(e.g., infrared and visible) image fusion. At last, we carry out some
experiments to evaluate the impact of these three algorithmic components on the
fusion performance when dealing with different applications. This article is
expected to serve as a tutorial and source of reference for researchers
preparing to enter the field or who desire to employ the sparse representation
theory in other fields.
