Blur Parameter Estimation of Resultant Blurred Images Using Cepstrum Analysis

In image processing workflows, we can employ blurring techniques to process images. One common approach involves applying blur effects to images and utilizing cepstrum analysis to estimate blur parameters from the resulting blurred images. Blur processing can intentionally obscure image details to achieve specific visual effects or meet particular application requirements. The cepstrum method serves as a fundamental image processing algorithm that analyzes spectral characteristics of images to estimate blur parameters. In implementation, this typically involves converting the image to frequency domain using Fast Fourier Transform (FFT), applying logarithmic operations to separate blur components, and performing inverse FFT to obtain the cepstrum where blur parameters become identifiable as distinct peaks. Through this methodology, we can gain deeper insights into image characteristics, enhance processing precision, and obtain more accurate analytical results. Key functions in MATLAB implementation would include fft2() for Fourier transformation, logarithmic operations for component separation, and ifft2() for cepstrum calculation, with peak detection algorithms identifying blur parameters in the resulting cepstral domain.