Gray Image extraction using Fuzzy Logic

TL;DR

This paper introduces a novel fuzzy rule-based method for gray image extraction that operates without external intervention, demonstrating superior performance over existing techniques using standard image quality metrics.

Contribution

The paper presents a new fuzzy rule-guided approach for gray image extraction that is fully unsupervised and outperforms other methods in accuracy and efficiency.

Findings

The proposed method achieves lower MSE and MAE compared to existing techniques.

It produces higher PSNR values, indicating better image quality.

Experimental results validate the effectiveness of the fuzzy rule-based approach.

Abstract

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).