Influence of multiplicative noise variance evaluation accuracy on mm-band SLAR image filtering efficiency

Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data

“…Third, although there can be initial assumptions on noise type and a range of variations of its statistical parameters, these parameters can be quite different even for a given imaging system depending upon conditions of its operation. The requirements to information accuracy on noise parameters are rather strict, e.g., variance of pure additive or pure multiplicative noise has to be known or pre-estimated with a relative error not larger than ±20% (Abramov et al, 2004). Thus, it is often desirable to estimate noise characteristics for a given image.…”

Methods for Blind Estimation of the Variance of Mixed Noise and Their Performance Analysis

“…The ratios (   ) 1/2 /σ μ 2 do not exceed 0.048. This does not cause serious problems in practice (Abramov et al, 2004). The main contribution to   results from estimation bias although contribution of 2   is also sufficient.…”

On required accuracy of mixed noise parameter estimation for image enhancement via denoising

“…This is just the case when a filtered image is perceived as having better visual quality. Dependences of optimal β for another HVS metric, PSNR-HVS-M (not presented in the paper), For more detailed analysis, we have further concentrated on two color images from the database TID2008 [41], namely the test image #3 (one of the simplest) and the test image #13 (the most complex one) (see Figure 1) since, according to our previous experience [17,18], just these marginal cases determine basic requirements. The test image #25 has not been chosen since it is artificial and we are more interested in enhancing natural scene images.…”

“…Moreover, for the methods of pure additive or multiplicative noise variance estimation, it has been established that the estimation relative error in practice should not be larger than ±20% [8,18]. If an obtained variance estimate is outside this limit, under-or oversmoothing takes place in image denoising based on blind estimation result.…”