The space-bandwidth product (SBP) limitation makes it difficult to obtain an image with both a high spatial resolution and a large field of view (FoV) through commonly used optical imaging systems. Although FoV and spectrum stitch provide solutions for SBP expansion, they rely on spatial and spectral scanning, which lead to massive image captures and a low processing speed. To solve the problem, we previously reported a physics-driven deep SBP-expanded framework (Deep SBP+) [J. Opt. Soc. Am. A 40, 833 (2023)JOAOD60740-323210.1364/JOSAA.480920]. Deep SBP+ can reconstruct an image with both high spatial resolution and a large FoV from a low-spatial-resolution image in a large FoV and several high-spatial-resolution images in sub-FoVs. In physics, Deep SBP+ reconstructs the convolution kernel between the low- and high-spatial-resolution images and improves the spatial resolution through deconvolution. But Deep SBP+ needs multiple high-spatial-resolution images in different sub-FoVs, inevitably complicating the operations. To further reduce the image captures, we report an updated version of Deep SBP+ 2.0, which can reconstruct an SBP expanded image from a low-spatial-resolution image in a large FoV and another high-spatial-resolution image in a sub-FoV. Different from Deep SBP+, the assumption that the convolution kernel is a Gaussian distribution is added to Deep SBP+ 2.0 to make the kernel calculation simple and in line with physics. Moreover, improved deep neural networks have been developed to enhance the generation capability. Proven by simulations and experiments, the receptive field is analyzed to prove that a high-spatial-resolution image in the sub-FoV can also guide the generation of the entire FoV. Furthermore, we also discuss the requirement of the sub-FoV image to obtain an SBP-expanded image of high quality. Considering its SBP expansion capability and convenient operation, the updated Deep SBP+ 2.0 can be a useful tool to pursue images with both high spatial resolution and a large FoV.