Multiband texture synthesis using histogram and power spectral density matching

摘要

In this work, we develop a new method for multispectral and hyperspectral texture synthesis using the multiband distribution and power spectral densities. Different approaches to this problem are mostly case specific and include histogram explosion, equalization in HSV or some other color space, and equalization based on the earth mover distance. For multiband images, the usual practice is to define the power spectral density for each band separately. While this captures the in-band autocorrelations, the cross-band correlations are not captured. Sometimes cross-psds are defined if it is known that cross-band correlations are important. However, as the number of bands increases, this method becomes computationally prohibitive. We propose a method that expresses psds for multiband images using a 3D fourier transform. An iterative scheme is used to equalize the histogram and psds for an input and target image. Our experiments show that the iteration tends to converge after 5-10 steps. The proposed method is computationally efficient and yields satisfactory results. We compare synthesized multispectral textures with real multispectral data.