Adaptive Image Sharpening Using Soft Computing: A Data-Driven Approach to Variable Blur Enhancement

The traditional Image enhancement methods are one of the problems in the pre-processing since these methods do not address the varying degrees of blurriness in the image dataset. As the amount of blur is different in each of the images, fixed-parameter sharpening methods may not solve the problem wisely. The wide spectrum of image processing and computer vision requires this kind of different pre-processing for different images based on the blurriness of the image. This paper presents a novel adaptive image sharpening framework that leverages fuzzy logic and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to intelligently determine optimal sharpening parameters based on a quantitative assessment of blur. Using the Laplacian variance method for blur detection, we categorise 1050 high-resolution images (6000 × 4000 pixels) into four fuzzy linguistic categories: very blurry, slightly blurry, acceptably sharp, and very sharp. A fuzzy rule-based system maps blur scores to adaptive sharpening strengths, which are then applied using a high-boost kernel derived from unsharp masking theory. The ANFIS model, trained on the blur-sharpening dataset using scikit-fuzzy, achieves a mean squared error of 1.27 in predicting optimal sharpening parameters. This result expresses the effectiveness of the method. The method could get rid of every blurry image, and at the same time, it shows that the increase in the number of sharp images is huge. This adaptive approach could prevent over-sharpening of already sharp images and under-enhancement of severely blurred images. Thus, the best outcome of this research is the robust solution it offers for automated image quality improvement in heterogeneous datasets.