Blur Detection Using Soft Computing Techniques: An Approach Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Ashwini S. Waghmare, Suhas S. Satonkar

Abstract: Blur detection is a critical task in digital image processing, particularly for applications in image restoration and enhancement. This paper presents a novel approach to blur detection using Adaptive Neuro-Fuzzy Inference System (ANFIS), a hybrid soft computing technique that combines fuzzy logic's reasoning with neural networks' learning ability. We evaluate the performance of the ANFIS-based blur detection method against traditional gradient-based and frequency-domain techniques. Experimental results show that the ANFIS model outperforms the traditional methods in terms of accuracy, precision, and robustness, making it a promising candidate for image restoration tasks.

Keywords: Blur Detection, Anfis, Image Processing, Soft Computing, Gradient Features, Frequency Features, Machine Learning, Image Restoration

How to Cite?: Ashwini S. Waghmare, Suhas S. Satonkar, "Blur Detection Using Soft Computing Techniques: An Approach Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 1668-1671, https://www.ijsr.net/getabstract.php?paperid=SR26128121717, DOI: https://dx.doi.org/10.21275/SR26128121717