Robust method for texture synthesis-by-analysis based on a multiscale Gabor scheme

We propose a new texture synthesis-by-analysis method inspired by current models of biological early vision and based on a multiscale Gabor scheme. The analysis stage starts with a log-polar sampling of the estimated power spectral density of the texture by a set of 4x4 Gabor filters, plus a low-pass residual (LPR). Then, for each channel, we compute its energy and its two (X,Y) bandwidths. The LPR is coded by five parameters. In addition, the density function ofthe original texture is also estimated and compressed to sixteen values. Therefore, texture is coded by only 69 parameters. The synthesis method consists of generating a set of 4x4 synthetic channels (Gabor filtered noise signals). Their energies and bandwidths are corrected to match the original features. These bandpass filtered noise signals are mixed into a single image. Finally, the histogram and LPR frequencies of the resulting texture are modified to fit the original values. We have obtained very satisfactory results both with highly random textures and with some quasi-periodic textures. Compared to previous methods, ours has other important advantages: high robustness (stable, non iterative and fully automatic), high compactness of the coding, and computational efficiency.