MATLAB Implementation of Image Blur Enhancement Techniques

This implementation demonstrates image blur enhancement techniques using MATLAB, encompassing both blurring and deblurring processing methods. The enhancement process involves applying controlled blurring operations followed by sophisticated deblurring algorithms to significantly improve image quality. Key MATLAB functions employed include fspecial() for creating blur filters, imfilter() for applying convolution operations, and deconvwnr() or deconvreg() for implementing Wiener or regularized deconvolution algorithms. Through these processing techniques, we can effectively enhance images by making them sharper and more defined with better edge preservation. Image blur enhancement serves as a powerful tool for quality improvement, making details more distinct and visible while simultaneously enhancing visual appeal. Using MATLAB for image blur enhancement enables straightforward processing of various image types, with flexible parameter adjustments for optimization. The implementation typically involves steps like Gaussian blur kernel creation, frequency domain analysis, and iterative restoration algorithms. This technology proves valuable for both professional image processing specialists and general users seeking to enhance image quality through computational methods.