Abstract
We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramer-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.
Highlights
A challenging problem of blind estimation of inherent sensor noise parameters [mainly its variance or standard deviationPaper 12159 received May 2, 2012; revised manuscript received Dec. 8, 2012; accepted for publication Jan. 11, 2013; published online Feb. 1, 2013.(STD)] from image data has been extensively studied by researchers for the last decade
This section deals with the application of the two designed estimators, noise informative (NI) þ fractal Brownian motion (fBm) and NI þ discrete cosine transform (DCT), of signal-dependent noise parameters to the NED2012 database of images that have been corrupted by signal-dependent noise
It has been shown that distribution of Fisher information (FI) with regard to noise standard deviation over the available image intensity range is mainly responsible for the accuracy of signal-dependent noise parameter estimation
Summary
A challenging problem of blind estimation of inherent sensor noise parameters [mainly its variance or standard deviationPaper 12159 received May 2, 2012; revised manuscript received Dec. 8, 2012; accepted for publication Jan. 11, 2013; published online Feb. 1, 2013.(STD)] from image data has been extensively studied by researchers for the last decade (see Ref. 1 and references therein). A challenging problem of blind estimation of inherent sensor noise parameters [mainly its variance or standard deviation. Sensor noise must be detected and quantified prior to the majority of subsequent image processing tasks. Such information can help to properly select a suitable technique or adjust a method parameter to a current noise level (unknown in advance), with the final goal of making these techniques operate well enough. 29 and 30, a method is proposed for estimating the denoising bounds for nonlocal filters from a noisy image, where noise statistics are to be known or accurately preestimated from the same noisy data. Similar results for local filters were obtained in Ref. 31, with the same requirement for noise statistics
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