A Randomized Approach for Patch-based Texture Synthesis using Wavelets

We present a wavelet-based approach for selecting patches in patch-based texture synthesis. We randomly select the first block that satisfies a minimum error criterion, computed from the wavelet coefficients (using 1D or 2D wavelets) for the overlapping region. We show that our wavelet-based approach improves texture synthesis for samples where previous work fails, mainly textures with prominent aligned features. Also, it generates similar quality textures when compared against texture synthesis using feature maps with the advantage that our proposed method uses implicit edge information (since it is embedded in the wavelet coefficients) whereas feature maps rely explicitly on edge features. In previous work, the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel-to-pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures.