An edge-guided image interpolation algorithm via directional filtering and data fusion

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 nonlinear 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

I. Introduction

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 LR digital imaging devices and the ever increasing computing power, interests in and demands for high-quality image interpolation algorithms have also increased.