Method for Estimating Motion Blur Direction and Motion Blur Length Parameters

This research aims to develop a method for estimating motion blur direction and length parameters in images. In this study, we will utilize computer vision techniques to achieve this objective. Motion blur is a significant concept in the field of image processing, as it refers to the blurring effect caused by relative motion between objects and the camera during exposure. By estimating motion blur parameters, we can better understand motion patterns in images and develop strategies to reduce or eliminate blur effects. The implementation typically involves analyzing the image spectrum in frequency domain, where motion blur manifests as distinctive directional patterns. Key algorithmic steps include: 1. Converting the image to grayscale and applying Fourier transform to obtain frequency domain representation 2. Identifying dominant directional components in the spectrum using Radon transform or Hough transform 3. Calculating blur length based on the spacing of spectral zeros or autocorrelation analysis 4. Implementing inverse filtering or Wiener deconvolution for parameter validation This method employs fundamental image processing functions such as fft2() for Fourier analysis, radon() for directional analysis, and morphological operations for precise parameter extraction. The accurate estimation of motion blur parameters holds significant application prospects in image restoration, forensic analysis, and computational photography.