Enhancement of Corneal Images using High Resolution Techniques

Abstract

This paper presents an enhancement method to deal with the medical images which have low resolution as corneal images that have hexagonal nature and contain edges which must be preserved. So its resolution should be increased to help ophthalmologists to accurately diagnose and monitor diseases. Image interpolation is employed for resolution enhancement. In this paper, we consider interpolation techniques such as: polynomial interpolation, adaptive polynomial interpolation, inverse interpolation, and super-resolution (SR) reconstruction in order to enhance corneal image interpolation-based and learning-based techniques. The polynomial interpolation includes: bilinear, bicubic, cubic spline and cubic O-MOMS as well as their adaptive techniques. While the inverse interpolation techniques comprises linear minimum mean square error (LMMSE), maximum entropy, and a regularized image interpolation. Although polynomial based techniques are the most popular due to their simplicity, they don’t take into account the local activity levels of the image to be interpolated and cause a blurring effect in data. Interpolation is applied to each pixel to adapt for changing local activity levels, this adaptation reduces the blur effect and provides a better visual quality. Polynomial interpolation and its adaptive techniques are called signal synthesis techniques because they are based on the use of known neighborhoods to synthesize unknown pixel values. However, they did not meet the prerequisites of modern sampling theory in the interpolation process, so a pre-filtering step is required in the reconstruction process; a correction filter is needed before the interpolation process to compensate for the non-ideality of the image acquisition model. This correction filter is obtained as the inverse of the cross-correlation sequence between the acquisition model filter and the reconstruction filter, hence the inverse interpolation technique is applied which is superior where low image degradation model is taken into consideration. Finally the SR technique is proposed and compared to other previous techniques. Simulations are conducted to investigate the performance of the considered proposed techniques. It is shown that the adaptive polynomial image interpolation methods and inverse interpolation techniques give satisfactory results in terms of peak signal-to-noise ratio (PSNR), while the proposed SR technique is the most superior.