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

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.