Image fusion based on multi‐scale transform and sparse representation: an image energy approach

Abstract

Image fusion is a process to enhance the human perception of different images from the same scene. Nowadays, two popular methods in the signal/image fusion, namely, multi-scale transform (MST) and sparse representation (SR) are being used. This study uses an image energy approach to enhance a fusion rule based on the combination of MST and SR methods. Each source image is first decomposed to its sub-bands using the selected MST method. Then, SR is applied to the low-pass band and maximum absolute (max-abs) rule merges the high-pass bands. The activity level of the sparse coefficients is measured based on the energy differences of the source images. When the gap energy is high enough, a coefficient with maximum L 2 -norm is selected; otherwise, maximum L 1 -norm is considered. Finally, by applying inverse MST to the attained bands, the fused image is reconstructed. The popular MSTs, such as discrete wavelet transform, dual-tree complex wavelet transform and non-sub-sampled contourlet are used. The experiments are carried out on several standard and real-life images. The measurement results confirm that the proposed method has enhanced the contrast, clarity and visual information of the fused results.