Fast Computation Methods for Image Power Spectral Density

This paper presents fast computation methods for image power spectral density, which allow selection of different filters and computation lengths for analysis. The implementation can leverage various filters through frequency domain multiplication, where users can specify filter types (e.g., Gaussian, Butterworth) and cutoff frequencies using function parameters like fspecial() in MATLAB or custom kernel definitions in Python. Computational length optimization involves zero-padding strategies and FFT size adjustments through parameters such as nfft in fft2() operations. Additionally, alternative mathematical models and algorithms like Welch's method or multitaper approaches can be incorporated to enhance calculation accuracy and efficiency through spectrum averaging and variance reduction techniques. The computation process can further employ parallel computing methods using GPU acceleration (CUDA/OpenCL) or multi-threaded FFT implementations (e.g., FFTW library) to significantly speed up processing. In summary, through improvements and optimizations in image power spectral density calculation methods, we can better understand and analyze frequency characteristics of images by examining spectral distributions and dominant frequency components.