Infrared and visible image fusion technology based on directionlets transform

Abstract

The article provides an infrared and visible image fusion algorithm based on directionlets transform. The registered original images were decomposed into the low-frequency and high-frequency coefficients by directionlets transform. Getting the mean of the low-frequency coefficients, applying the local variance maximum principle to the high-frequency coefficients, thereby the fusion coefficients of the fused image can be acquired. Finally, the fused image was obtained using inverse directionlets transform. The experiment indicates that the fusion algorithm can extract the original image features better. Compared with the traditional fusion algorithms, the fusion algorithm presented in this article provides better subjective visual effect, and the standard deviation and entropy value would be somewhat increased.

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

“…The transform domain-based fusion method is the current mainstream infrared and visible light image fusion method, whose main idea is to map the source images to be fused from the spatial domain into one sparser transform domain, conduct corresponding fusion in this transform domain according to some fusion principles, and acquire the fusion results after inverse transformation. The main related methods include the Wavelet Transform (WT) [5], Curvelet Transform (CT) [6], Non-Sampled Contourlet Transform (NSCT) [7], Non-Sampled Shearlet Transform (NSST) [8], [9], Directionlet Transform [10], empirical mode decomposition [11], internal generative mechanism [12], multiresolution singular value decomposition [13], Tetrolet Transform (TT) [14], Top-hat transform [15], Sparse Representation (SR) [16] and Total Variation (TV) decomposition method [17]- [19]. The spatial domain method directly extracts useful information from fusion in the spatial domain without a decomposition or reconstruction step and includes the significance fusion method and subspace-based fusion method [8], [20]- [22].…”

Directionlets Using In-Phase Lifting for Image Representation

“…Both these conceptually similar methods apply spatially varying re-sampling followed by separable filtering, and hence, are forced to process each image segment independently. Recent works on directionlets focus on its application in solving different image processing problems like despeckling [11], watermarking [12], enhancement [13], fusion [14], etc. In this work, we study the limitations of directionlets due to independent processing of image segments, and give a lifting based implementation to overcome them.…”