Detection of low-contrast images in film-grain noise

Image preprocessing for rotation-invariant pattern recognition in the presence of signal-dependent noise

I propose a new method that ensures efficient rotation-invariant pattern recognition in the presence of signal-dependent noise by combining the application of rotation-invariant correlation filters with preprocessing of the noisy input images. The preprocessing uses local suboptimal estimators derived from estimation theory and implies an a priori knowledge of a model describing the noise source. The image noise sources considered are speckle and film-grain noise. Pour different metrics are used to analyze the correlation performance of the circular-harmonic filter, the phase-only circular-harmonic filter, and the binary phase-only circular-harmonic filter, with and without a preprocessing. Computer simulations show that signal-dependent noise can seriously degrade the performance of the phase-only circular-harmonic filter and the binary phase-only circular-harmonic filter. The most severe indication of correlation-performance degradation is the occurrence of false alarms in 15% to 20% of noise realizations of the correlation. Preprocessing increases the correlation-peak signal-to-noise ratio significantly and reduces the false-alarm probability by one to two orders of magnitude.

Exact variance-stabilizing transformations for image-signal-dependent Rayleigh and other Weibull noise sources

Variance-stabilizing transforms for families of Rayleigh and, more generally, for Weibull random variables are derived and shown to be exact. These transforms, when applied to appropriate noisy images, render signal-dependent noise signal-independent. Their utility is that, after applying the transforms, classical estimation procedures devised for additive, signal-independent noise can be brought to bear. The results are expected to find use in image filtering, in photon counting, and in the processing of optical field magnitudes and intensities generated by chaotic sources.

A model for radar images and its application to adaptive digital filtering of multiplicative noise

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.

Transformation of image-signal-dependent noise into image-signal-independent noise

A point transformation, the normalizing transform, is presented, which, when applied to a measured noisy image, renders its noise signal independent. The transform is suitable for arbitary noise-to-signal dependence. We demonstrate its applicability and its limitations by using, as an example, noisy signals that belong to a family of gamma-distributed random variables with power-law variance-to-mean dependence. Its normalizing and variance-stabilizing properties are studied.