Image Fusion with Convolutional Sparse Representation

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion during the last decade. However, due to the patch-based manner adopted in standard SR models, most existing SR-based image fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to mis-registration, while these two issues are of great concern in image fusion. We introduce a recently emerged signal decomposition model known as convolu

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2110.html