Fusion framework for multi-focus images based on compressed sensing

In this study, an efficient image fusion framework for multi-focus images is proposed based on compressed sensing. The new fusion framework consists of three parts: image sampling, measurement fusion and image reconstruction. First, the dual-channel pulse coupled neural network model is used in the image sampling part as an important weighting factor in the fusion scheme. Second, the result from the measurement fusion part is reconstructed through a new reconstruction algorithm called self-adaptively modified Landwebber filter. Finally, computer simulation-based experiment is conducted, showing that the novel fusion framework is capable of saving computational resource and enhancing the fusion result and is easy to implement.