<p>Defocus blur often arises in images captured with wide apertures and shallow depth of field, presenting significant challenges due to its spatial variability and difficulty in estimation. Existing methods for defocus and deblurring typically address overall blur but struggle with varying blur effects across different depths of field. To tackle this issue, we propose a novel approach that estimates defocus maps and performs multi-scale defocus deblurring through cross-stage fusion of multi-scale depth-of-field images. Our method, inspired by optimizing the pupil mask in monocular ranging, utilizes all-pixel dual-core focus sensing technology to estimate defocus blur for each pixel. We independently solve for the blur kernels of left and right parallax images, establishing a connection between depth of field and defocus in the form of a defocus map. This approach enables the recovery of fully focused images through multi-scale depth-of-field cross-stage fusion. By leveraging global information features from dual-pixel image sensing, our method significantly improves the handling of defocus blurring across different depth-of-field scales and outperforms recent defocus and deblurring methods in both quantitative and qualitative assessments.</p>