Measuring the Global Phase Coherence of an image

Abstract

The Fourier phase spectrum of an image is well known to contain crucial information about the image geometry, in particular its contours. In this paper, we show that it is also strongly related to the image quality, in particular its sharpness. We propose a way to define the Global Phase Coherence (GPC) of an image, by comparing the likelihood of the image to the likelihood of all possible images sharing the same Fourier power spectrum. The likelihood is measured with the total variation (Rudin-Osher-Fatemi implicit prior), and the numerical estimation is realized by a Monte-Carlo simulation. We show that the obtained GPC measure decreases with blur, noise, and ringing, and thus provides a new interesting sharpness indicator, that can be used for parametric blind deconvolution, as demonstrated by experiments.

No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information

“…As a sharp (or noise-free) image is much more sensitive to phase degradations than a blurry (or noisy) image, such a characterization of phase coherence is directly related to image quality. This approach led to the definition of three phase coherence measures, namely the Global Phase Coherence [3], the Sharpness Index [4], and the index S [22]. The present paper gives a more detailed and merged discussion about these global phase coherence indices.…”

“…This Section presents the detailed construction of the phase coherence indices introduced in [3,4,22].…”

“…1, we reproduce the experiment which consists in exchanging the moduli of two images: as can be seen, the geometry of the image whose phase was kept persists. From there, in an a contrario framework, the authors of [3] define the global phase coherence (GPC) by measuring how much the geometry is affected when the phase information is lost.…”

“…In 2008, a notion of global phase coherence was proposed [3], and related to image sharpness. The idea was to use a kind of a contrario framework 1 [10] to quantize how much the regularity of the image (more precisely, its total variation) was affected by the destruction of the phase information.…”

An image-inspired audio sharpness index

“…In image processing, several reference-free objective measures of sharpness are based on the importance of Fourier (or wavelet) phase in the perception of blur [10]. In particular, the global phase coherence (GPC, [11]) measures how the regularity of an image -defined by its total variation (TV)-is affected by the destruction of the phase information. The study presented in [12] proposes an new Sharpness Index (SI), computationally simpler but shown to behave similarly to the GPC.…”