Combined Sparsifying Transforms for Compressive Image Fusion

Abstract

In this paper, we present a new compressive image fusion method based on combined sparsifying transforms. First, the framework of compressive image fusion is introduced briefly. Then, combined sparsifying transforms are presented to enhance the sparsity of images. Finally, a reconstruction algorithm based on the nonlinear conjugate gradient is presented to get the fused image. The simulations demonstrate that by using the combined sparsifying transforms better results can be achieved in terms of both the subjective visual effect and the objective evaluation indexes than using only a single sparsifying transform for compressive image fusion

Comparison of Inter-and Intra-Subject Variability of P300 Spelling Dictionary in EEG Compressed Sensing

“…The difficulties for using in applications of such concepts are related to the way such results are perceived, in a more or less intuitive manner, in order to facilitate the fusion between theory and applications. The literature of recent years shows a large number of papers in the CS field [2,3], covering both 1D and 2D medical signals [4,5,6]. Among the 1D signals currently used in CS applications, are the electrocardiogram (ECG) and electroencephalogram (EEG) since they are commonly used in the medical world as well.…”