Super-resolution from observations with variable zooming ratios

Super-resolution reconstruction (SR) is a technique for estimating a high resolution (HR) image from multiple low resolution (LR) copies captured from the same scene. Most of the existing SR algorithms are based on the assumption that the scene moves parallel to the camera lens with translational or rotational motion. However, such an assumption may not be held if zooming exists when acquiring LR images. We present in this paper a new linear model to represent the relationship between the HR image and the LR images captured with arbitrary sampling lattices. Based on this model, a MAP based SR algorithm is proposed. Experimental results verify the improvements on the visual quality of our framework.