Restoration of Motion-Blurred Images

The restoration of motion-blurred images involves several critical steps. First, it is essential to determine both the blur direction and blur scale of the motion-affected image. This initial phase requires careful analysis of the blur effects present in the image, involving precise estimation of both directional and magnitude parameters of the blur. In practical implementations, this often utilizes techniques like Radon transform or cepstrum analysis to automatically detect blur characteristics from the degraded image. Following this identification process, we implement the Wiener filtering algorithm to achieve image restoration. Wiener filtering serves as a fundamental image recovery method that effectively reduces both noise and blur effects in images, resulting in enhanced clarity and sharpness. The algorithm works by incorporating statistical properties of both the original image and noise, creating an optimal filter in the frequency domain that minimizes mean square error. Through the restoration of motion-blurred images, we can achieve superior visual quality while significantly improving both image quality and overall visibility. Implementation typically involves Fourier domain operations where the Wiener filter is applied after precise blur parameter estimation.