Singular value decomposition and saliency - map based image fusion for visible and infrared images

Abstract

ABSTRACT Multiple sensors capture many images and these images are fused as a single image in many applications to obtain high spatial and spectral resolution. A new image fusion method is proposed in this work to enhance the fusion of infrared and visible images. Image fusion methods based on convolutional neural networks, edge-preserving filters and lower rank approximation require high computational complexity and it is very slow for complex tasks. To overcome these drawbacks, singular value decomposition (SVD) based image fusion is proposed. In SVD, accurate decomposition is performed and most of the information is packed in few singular values for a given image. Singular value decomposition decomposes the source images into base and detail layers. Visual saliency and weight map are constructed to integrate information and complimentary information into detail layers. Statistical techniques are used to fuse base layers and the fused image is a linear combination of base and detail layers. Visual inspection and fusion metrics are considered to validate the performance of image fusion. Testing the proposed method on several image pairs indicates that it is superior or comparable to the existing methods.