  Real-world data processing problems often involve various image modalities
associated with a certain scene, including RGB images, infrared images or
multi-spectral images. The fact that different image modalities often share
certain attributes, such as certain edges, textures and other structure
primitives, represents an opportunity to enhance various image processing
tasks. This paper proposes a new approach to construct a high-resolution (HR)
version of a low-resolution (LR) image given another HR image modality as
reference, based on joint sparse representations induced by coupled
dictionaries. Our approach, which captures the similarities and disparities
between different image modalities in a learned sparse feature domain in
\emph{lieu} of the original image domain, consists of two phases. The coupled
dictionary learning phase is used to learn a set of dictionaries that couple
different image modalities in the sparse feature domain given a set of training
data. In turn, the coupled super-resolution phase leverages such coupled
dictionaries to construct a HR version of the LR target image given another
related image modality. One of the merits of our sparsity-driven approach
relates to the fact that it overcomes drawbacks such as the texture copying
artifacts commonly resulting from inconsistency between the guidance and target
images. Experiments on real multimodal images demonstrate that incorporating
appropriate guidance information via joint sparse representation induced by
coupled dictionary learning brings notable benefits in the super-resolution
task with respect to the state-of-the-art. Of particular relevance, the
proposed approach also demonstrates better robustness than competing
deep-learning-based methods in the presence of noise.
