Feature Enhancement Method for Fuzzy Image Using Mathematical Morphology and Deep Learning

Abstract

High-definition images can provide valuable reference for research in fields such as medical image analysis and machine vision. Therefore, image feature enhancement is performed to obtain high-quality images. Conventional methods for image feature enhancement extract features only from a single scale, resulting in low image information entropy. To address this issue, a fuzzy image feature enhancement method using mathematical morphology and deep learning (FIFEMD) is proposed. First, a near-infrared transmission light source is used as the main light source to construct a capture platform, and the grayscale values are normalized and filtered. Second, basic mathematical morphology operations are employed to remove noise points in the original image, and a separable residual dense block is used to extract multi-scale features from the image. Finally, the generated image features are fused and reconstructed using the Hessian matrix method to achieve image feature enhancement. Experimental results demonstrate that the images processed using the FIFEMD method have higher information entropy values, with entropy values above 6.75. The processing time is approximately 2.5 s, indicating high efficiency. Therefore, the FIFEMD method can achieve better image feature enhancement.

Keywords:
Fuzzy images; feature enhancement; mathematical morphology; deep learning; information entropy

HIGHLIGHTS

We propose a fuzzy image feature enhancement method.

By utilizing separable residual dense blocks to enhance the effectiveness of image understanding.

The Hessian matrix method can improve the accuracy of image analysis.