Image Fusion Using LEP Filtering and Bilinear Interpolation

TL;DR

This paper introduces a fast image fusion method using LEP filtering and bilinear interpolation, producing high-quality, noise-reduced images suitable for medical applications by preserving edges and details.

Contribution

It proposes a novel fusion technique combining LEP filtering with a two-scale image representation for improved image quality and noise reduction.

Findings

High PSNR and SSIM values indicate low noise and high image quality.

Effective edge preservation without structural alteration.

Suitable for medical image processing applications.

Abstract

Image Fusion is the process in which core information from a set of component images is merged to form a single image, which is more informative and complete than the component input images in quality and appearance. This paper presents a fast and effective image fusion method for creating high quality fused images by merging component images. In the proposed method, the input image is broken down to a two-scale image representation with a base layer having large scale variations in intensity, and a detail layer containing small scale details. Here fusion of the base and detail layers is implemented by means of a Local Edge preserving filtering based technique. The proposed method is an efficient image fusion technique in which the noise component is very low and quality of the resultant image is high so that it can be used for applications like medical image processing, requiring very accurate edge preserved images. Performance is tested by calculating PSNR and SSIM of images. The benefit of the proposed method is that it removes noise without altering the underlying structures of the image. This paper also presents an image zooming technique using bilinear interpolation in which a portion of the input image is cropped and bilinear interpolation is applied. Experimental results showed that the when PSNR value is calculated, the noise is found to be very low for the resultant image portion.