The invention relates to a method of controlling an action using a measurement taken on at least one digital image (10) containing at least two colours (195, 196) and taken with an image capture device. The inventive method consists in: measuring (190) the relative sharpness between at least two colours on at least one region (R) of the image, and controlling (191) at least one action as a function of the measured relative sharpness.

Method of controlling an action, such as a sharpness modification, using a colour digital image

A method for evaluating texture quality as shot by a camera is presented. It is shown that usual sharpness measurements
are not completely satisfying for this task. A new target based on random geometry is proposed. It uses the so-called
dead leaves model. It contains objects of any size at any orientation and follows some common statistics with natural
images. Some experiments show that the correlation between objectives measurements derived from this target and
subjective measurements conducted in the Camera Phone Image Quality initiative are excellent.

Measuring texture sharpness of a digital camera

We describe the procedure to evaluate the image quality of a camera in terms of texture preservation. We use a
stochastic model coming from stochastic geometry, and known as the dead leaves model. It intrinsically reproduces
occlusions phenomena, producing edges at any scale and any orientation with a possibly low level of contrast. An
advantage of this synthetic model is that it provides a ground truth in terms of image statistics. In particular, its power
spectrum is a power law, as many natural textures. Therefore, we can define a texture MTF as the ratio of the Fourier
transform of the camera picture by the Fourier transform of the original target and we fully describe the procedure to
compute it. We will compare the results with the traditional MTF (computed on a slanted edge as defined in the ISO
12233 standard) and will show that the texture MTF is indeed more appropriate for describing fine detail rendering.

Dead leaves model for measuring texture quality on a digital camera

Fractional derivative based techniques have been proposed for preprocessing of digital images. Although these techniques address the texture enhancement and other issues to a certain extent, none of them have proposed a method of determining the fractional order adaptively. In this paper, we propose a Grunwald-Letnikov derivative based fractional derivative mask for image contrast enhancement. The proposed mask is multidirectional thus enhancing the image in several directions in one pass. The regularisation based prediction network learns from the training set of images and determines the fractional order based on the statistics of the image at hand. Also the blur reduction is achieved in a controlled fashion as the fractional order is predicted according to the desired blur improvement. Experimental results with the comparative blur metric show the effectiveness of the proposed novel filter on a wide range of images. (6 pages)

Fractional derivative filter for image contrast enhancement with order prediction

The invention relates to the processing of a digital object that comprises: cancelling the noise of an original object (I) of a first type containing noise in order to obtain a noise-free object (J) of the first type; obtaining an object with a quasi-white noise of the first type from a difference (B) between the original object and the noise-free object; applying to the noise-free object (J) a first processing (t1) that comprises a neighbouring processing for obtaining a transformed object (K) of a second type, the first processing being such that it would structure the noise contained in the original object if it was applied to said original object; applying to the noise object a second white processing (t2) for obtaining a quasi-white transformed noise object (C) of the second type; and inserting into the transformed object (K) the transformed noise object.

Method for processing a digital object and related system

After discussing usual approaches to measuring blur, we show theoretically that there is essentially a unique way to quantify blur by a single number and we confirm the usefulness of that measure by some experiment on a natural image.

Uniqueness of blur measure

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Uniqueness of blur measure