Due to the difficulty in acquiring massive task-specific occluded images, classification of images with deep convolutional neural networks (CNNs) remains highly challenging. To alleviate dependency on large-scale image datasets, we propose a novel approach improve accuracy by fine-tuning pre-trained models set augmented feature vectors (DFVs). The DFVs is composed original and pseudo-DFVs. pseu...

This paper presents a multimodal image fusion method using novel decomposition model based on coupled dictionary learning. The proposed is general and can be used for variety of imaging modalities. In particular, the images to fused are decomposed into correlated uncorrelated components sparse representations with identical supports Pearson correlation constraint, respectively. resulting optimi...