Abstract:
Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high resolution image from a low resolution counterpart. We propose a new edge-guided non-linear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts.
Many users of digital images desire to improve the native resolution offered by imaging hardware. Image interpolation aims to reconstruct a higher resolution (HR) image from the associated low resolution (LR) capture. It has applications in medical imaging, remote sensing and digital photographs [3-5], etc. A number of image interpolation methods have been developed [1-2, 5-6, 8-16]. While the commonly used linear methods, such as pixel duplication, bilinear interpolation and bicubic convolution interpolation, have advantages in simplicity and fast implementation [7], they suffer from some inherent defects, including block effects, blurred details and ringing artifacts around edges. With the prevalence of inexpensive and relatively low resolution digital imaging devices and the ever increasing computing power, interests in and demands for high-quality image interpolation algorithms have also increased.
The human visual systems are highly sensitive to edge structures, which convey much of the image semantics, so a key requirement for image interpolation algorithms is to faithfully reconstruct the edges in the original scene. The traditional linear interpolation methods [1-3, 5-6] do not work very well under the edge preserving criterion. Some nonlinear interpolation techniques [8-15] were proposed in recent years to maintain edge sharpness. The interpolation scheme of Jensen and Anastassiou [8] detects edges and fits them by some templates to improve the visual perception of enlarged images. Li and Orchard [9] used the covariance of the LR image to estimate the HR image covariance, which represents the edge direction information to some extent, and proposed a Wiener-filtering like interpolation scheme. Since this method needs a relatively large window to compute the covariance matrix for each missing sample, it may introduce some artifacts in local structures due to sample statistics change and hence the incorrect estimation of covariance. The image interpolator by Carrato atterns and optimizing the parameters in the operator. Muresan [15] detected the edge in diagonal and non-diagonal directions and then recovered the missing samples along the detected direction by using 1-D polynomial interpolation.