Cubic convolution for super-resolution from microscanned images

This paper presents a computationally efficient method for super-resolution reconstruction and restoration from microscanned images. Microscanning creates multiple low-resolution images with slightly varying sample-scene phase shifts. Microscanning can be implemented with a physical microscanner built into specialized imaging systems or by simply panning and/or tilting traditional imaging systems to acquire a temporal sequence of images. Digital processing can combine the low-resolution images to produce an image with higher pixel resolution (i.e., super-resolution) and higher fidelity. The cubic convolution method developed in this paper employs one-pass, small-kernel convolution to perform reconstruction (increasing resolution) and restoration (improving fidelity). The approach is based on an end-to-end, continuous-discrete-continuous model of the microscanning imaging process. The derivation yields a parametric form that can be optimized for the characteristics of the scene and the imaging system. Because cubic convolution is constrained to a small spatial kernel, the approach is efficient and is amenable to adaptive processing and to parallel implementation. Experimental results with simulated imaging and with real microscanned images indicate that the cubic convolution method efficiently and effectively increases resolution and fidelity for significantly improved image quality.