Digital image capture devices such as digital cameras and scanners have an associated image resolution. In many applications, it may be necessary or desirable to obtain a higher resolution version of a digital image than is supported by the digital image capture device. For example, if it is desired to print a digital image having one resolution on a digital printer having a higher resolution, it is necessary to somehow up-sample the digital image data.
The simplest methods for increasing the resolution of an image is to use an interpolation method to estimate new pixel values at the higher resolution. Well-known interpolation methods such as nearest neighbor, bilinear and bicubic interpolation algorithms are commonly used for this purpose. However, the result of such algorithms is that the interpolated images tend to be lower in quality than an image captured using a digital image capture device having the higher resolution. For example, a sharp edge transition in a 300 dpi image will be turned into a “blurred” edge transition in a 600 dpi image determined using bilinear interpolation.
Interpolation methods are fundamentally limited by the well-known Nyquist-Shannon sampling theory which implies that it is impossible to reconstruct information about spatial frequencies higher than the Nyquist frequency (half the sampling frequency) without a priori knowledge about the scene.
Various methods for determining higher resolution images have been developed which attempt to overcome the limitation of the Nyquist-Shannon sampling theory by incorporating additional image information. These techniques are generally referred to as “super resolution” algorithms.
Some techniques such as that disclosed by Kuo et al. in U.S. Pat. No. 7,583,860, entitled “Method for producing enhanced-resolution image by use of a plurality of low-resolution images,” reconstruct a high-resolution image by using information for a plurality of low-resolution images.
In U.S. Pat. No. 7,106,914 entitled “Bayesian image super resolution,” Tipping et al have disclosed a method for determining the most likely high-resolution image given a set of multiple low-resolution images using a Bayesian inference model.
U.S. Pat. No. 7,477,802 to Milanfar et al., entitled “Robust reconstruction of high resolution grayscale images from a sequence of low resolution frames” discloses a method for creating a super-resolution image from a set of lower-resolution images which incorporates an energy function having a data fidelity penalty term and a spatial penalty term that encourages sharp edges in the high-resolution image.
While methods that utilize multiple images have utility for applications such as video which involve capturing multiple images, they are not useful for cases where only a single input image is available.
In the article “Image super-resolution using gradient profile prior” (IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008), J. Sun et al. have disclosed a method for determining a high resolution image from a single low-resolution image. This technique involves analyzing a gradient map to determine a parametric model describing the shape and sharpness of gradients occurring in the image. The edge gradient information can then be used to form a high-resolution image that preserves the edge characteristics.
In the article “Example-based super-resolution” (IEEE Computer Graphics and Applications, Vol. 22, pp. 56-65, 2002), W. T. Freeman et al. have disclosed a training-based super-resolution algorithm. The algorithm involves performing a nearest-neighbor search in a training set for a vector derived from each patch of local image data.