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

TLDR

Preserving edge structures is a challenge for image interpolation algorithms that reconstruct high‑resolution images from low‑resolution inputs. The authors propose a new edge‑guided nonlinear interpolation technique that combines directional filtering with data fusion. For each pixel, two orthogonal observation sets generate directional estimates that are fused by a linear minimum‑mean‑square‑error estimator, and a simplified version reduces computational cost while maintaining performance. Experiments demonstrate that the proposed methods preserve edge sharpness and reduce ringing artifacts.

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 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.