Multimodal Image Fusion Method Based on Guided Filter

In the process of multimodal image fusion, how to improve the visual effect after the image fused, while taking into account the protection of energy and the extraction of details, has attracted more and more attention in recent years. Based on the research of visual saliency and the final action-level measurement of the base layer, a multimodal image fusion method based on a guided filter is proposed in this paper. Firstly, multi-scale decomposition of a guided filter is used to decompose the two source images into a small-scale layer, large-scale layer and base layer. The fusion rule of the maximum absolute value is adopted in the small-scale layer, the weight fusion rule based on regular visual parameters is adopted in the large-scale layer and the fusion rule based on activity-level measurement is adopted in the base layer. Finally, the fused three scales are laminated into the final fused image. The experimental results show that the proposed method can improve the image edge processing and visual effect in multimodal image fusion.

Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images

The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments.