Abstract
Abstract Characteristics of noise (type, statistics, spatial correlation) are nowadays exploited in many image denoising and enhancement methods. However, these characteristics are often unknown, and they have to be extracted from an image at hand. There are many powerful and accurate blind methods for noise variance estimation for the cases of additive and multiplicative noise models. However, more complicated noise models containing a mixture of signal-independent (SI) and signal-dependent (SD) components are often more adequate in practice. Parameters of both components have to be automatically estimated to be used in image enhancement. This paper addresses a question of required accuracy of such estimation. Analysis is carried out for color images processed by a filter based on discrete cosine transform. The influence of errors in mixed noise parameters estimation is studied in terms of filtering efficiency. This efficiency is characterized by the conventional criterion peak signal-to-noise ratio (PSNR) and two visual quality metrics, PSNR human visual system masking (PSNR-HVS-M) and multi-scale structural similarity (MSSIM). If a reduction of filtering efficiency exceeds 0.5 dB (in terms of PSNR and PSNR-HVS-M) or 0.005 (in terms of MSSIM), mixed noise parameters estimation is assumed to be unacceptable. As the result, it is shown that SI and SD noise parameters have to be estimated with a relative error not exceeding 20%…30%.
Highlights
Imaging systems are widely used in various applications such as remote sensing, non-destructive control, medical diagnostics, photography, etc. [1]
In order to enhance an image using, e.g., modern image denoising methods based on wavelets [5,6,7] or discrete cosine transform (DCT) [8,9] transforms, one has to know a noise type and its basic characteristics such as probability density function (PDF), variance, or two-dimensional (2D) spatial correlation function (if the observed noise is not independent and identically distributed (i.i.d.))
It is demonstrated that the parameter that corresponds to a dominant noise type has to be estimated with a higher accuracy
Summary
Imaging systems (sensors) are widely used in various applications such as remote sensing, non-destructive control, medical diagnostics, photography, etc. [1]. To avoid difficulties of polynomial order choice, we consider below a simplest case of mixed noise where SD and SI components are characterized by one parameter each This model is typical in raw data in digital photos [9,11], sub-band images of hyperspectral remote sensing data [15,16] and radar images formed by multi-look SARs and side-look aperture radars [21,22]. We have dependencies that are fully described by two parameters, σ2si and k, that have to be estimated in a blind manner and used in filtering (we assume that we know a priori which model, (1) or (2), fits the data) In both cases, it is possible to find such Ittr that for Itr > Ittr the SD component is dominant and vice versa.
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