Image Blurring using Defocus or Motion Blur Models with Restoration Techniques

1. Select an image and apply blurring processing. You can use either defocus model or motion blur model to blur the image, then employ inverse filtering to restore the blurred image. Finally, display the original image, blurred image, and restored image. Code implementation typically involves creating a PSF kernel (using functions like fspecial in MATLAB or Gaussian kernels for defocus, linear motion kernels for motion blur), applying convolution via imfilter or conv2, and performing frequency-domain division for inverse filtering with careful handling of zero-frequency components.

2. Besides using inverse filtering for restoration, you can also implement iterative deconvolution algorithms (such as Richardson-Lucy or blind deconvolution) for blurred image restoration. Similarly, display the original, blurred, and restored images. Iterative methods often require parameters like number of iterations and regularization terms, implemented through while/for loops with convergence checks to progressively refine the estimate while controlling noise amplification.

In both steps, by adding detailed descriptions and explanations of the algorithmic implementations and parameter selections, we can more comprehensively understand the blur image processing workflow and evaluate the effectiveness of different restoration techniques through quantitative metrics like PSNR or SSIM.