Infrared and low-light-level image fusion based on ℓ2-energy minimization and mixed-ℓ1-gradient regularization

Abstract

In order to compensate for the visual defect of the low-light-level image and combine the saliency features of the infrared image, this paper proposes an infrared and low-light-level image fusion model based on ℓ2-energy minimization and mixed-ℓ1-gradient regularization. First, this novel model uses the non-subsampled shearlet transform (NSST) as a multi-scale decomposition tool to capture the low and high-frequency components of the source images. Because the NSST has good localization characteristics, excellent directional selectivity, parabolic edge characteristics, and translation invariance, it is more suitable for image decomposition and reconstruction. Secondly, for the low-frequency components that reflect the energy information, an optimization model based on ℓ2-energy minimization is adopted as its fusion rule. This new rule allows the fused image to have similar pixel intensities to the given infrared image, thus improving the visual observation of the fused image and reducing the influence of the brightness defect under weak light. Thirdly, considering that the ℓ1-norm encourages the sparseness of the gradients, this paper uses the ℓ1-gradient regularization to guide the fusion of high-frequency components. This method can greatly restore the gradient features hidden in the source images to the fused image so that the fused image will have clearer edge details. In order to verify the effectiveness of the proposed algorithm, we adopted 6 × 6 independent fusion experiments. The final experimental results show that the proposed algorithm has better visual effects in the fusion problem of low-light-level environment, and the performance of objective evaluation is also good, which is better than other existing typical methods.