Gradient-based multi-focus image fusion method using convolution neural network

Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods. Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods.