Motion Blur Image Generation and Inverse Filtering

Generation of motion-blurred images and comparative analysis of restoration effects using inverse filtering and Wiener filtering. In the field of image processing, motion blur is a common phenomenon that causes images to lose clarity and detail. To restore the sharpness of blurred images, techniques such as inverse filtering and Wiener filtering can be employed. Inverse filtering is a widely used image restoration method that reverses the blurring process by estimating the blur kernel (point spread function). Implementation typically involves frequency domain operations using Fast Fourier Transform (FFT), where the blurred image's Fourier transform is divided by the blur kernel's Fourier transform. However, inverse filtering often produces artifacts when processing noisy images due to amplification of high-frequency noise components. In contrast, Wiener filtering provides a more robust image restoration approach that optimizes recovery results by considering statistical characteristics of both the blur kernel and image noise. The Wiener filter implementation incorporates a noise-to-signal power ratio parameter, which acts as a regularization term to prevent noise amplification. This frequency-domain filter can be expressed as G(u,v) = H*(u,v) / (|H(u,v)|² + K), where H(u,v) represents the blur kernel's Fourier transform and K denotes the noise power spectral density. When restoring motion-blurred images, Wiener filtering typically yields superior results by effectively balancing detail recovery and noise suppression, making it particularly suitable for practical applications with significant noise contamination.