International audienceDesigning realistic noise patterns from scratch is hard. To solve this problem, recent contributions have proposed involved spectral analysis algorithms that enable procedural noise models to faithfully reproduce some class of textures. The aim of this paper is to propose the simplest and most efficient noise model that allows for the reproduction of any Gaussian texture. Texton noise is a simple sparse convolution noise that sums randomly scattered copies of a small bilinear texture called texton. We introduce an automatic algorithm to compute the texton associated with an input texture image that concentrates the input frequency content into the desired texton support. One of the main features of texton noise is that its evaluation only consists to sum thirty texture fetches on average. Consequently texton noise generates Gaussian textures with an unprecedented evaluation speed for noise by example. A second main feature of texton noise is that it allows for high quality on-the-fly anisotropic filtering by simply invoking existing GPU hardware solutions for texture fetches. In addition, we demonstrate that texton noise can be applied on any surface using parameterization-free surface noise and that it allows for noise mixing

It has been known for more than 30 years that most of the geometric content of a digital image is encoded in the phase of its Fourier transform. This has led to several works that exploit the global (Fourier) or local (Wavelet) phase information of an image to achieve quality assessment, edge detection, and, more recently, blind deblurring. We here propose a deeper insight into three recent sharpness metrics (Global Phase Coherence, Sharpness Index and a simplified version of it), which all measure in a probabilistic sense the surprisingly small total variation of an image compared to that of certain associated random-phase fields. We exhibit several theoretical connections between these indices, and study their behavior on a general class of stationary random fields. We also use experiments to highlight the behavior of these metrics with respect to blur, noise and deconvolution artifacts (ringing). Finally, we propose an application to isotropic blind deblurring and illustrate its efficiency on several examples.