Impulse Noise Removal Using Unbiased Weighted Mean Filter

Digital imaging technology has provided countless opportunities for human visual applications and many scientific disciplines such as astronomy and microbiology. Digital images are subject to various noise due to environmental factors or faults in hardware. One type of noise — impulse noise — manifests itself with the highest or the lowest intensity value in the dynamic range during digitization process. Impulse noise involves high frequency components which are undesirable. Therefore, it is vital to restore contaminated digital images before utilizing them in various applications. In this thesis, we have investigated Nonlinear Fixed-Valued Impulse (salt-and-pepper) Noise removal methods. Restoration of a contaminated image is composed of two stages. These are noise detection and restoration. The performance of various state-ofthe- art impulse noise removal methods are empirically compared for these two stages. For detection, misclassification and false-alarm rates are used for objective measurement. Restoration capabilities are compared in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Mean Absolute Error (MAE). We have also identified a common problem among impulse noise removal methods, namely, spatial bias. Asymmetric distribution of corruption prevents an equal contribution of uncorrupted pixels in the filtering window from a spatial perspective, effectively yielding a biased estimation of the original intensity value. In order to eliminate spatial bias, we have proposed Unbiased Weighted Mean Filter (UWMF). UWMF eliminates spatial bias by recalibrating pixel weights based on the positional distribution of corrupted pixels in the filtering window. Recalibrated weights reflect the spatial properties of corruption and compensate the missing contribution. iii We have demonstrated that elimination of spatial bias improves restoration quality in terms of objective measurements (PSNR, SSIM and MAE). In addition, unbiased restoration results with least amount of disturbance in the edges and smooth regions. Keywords: Impulse Noise Removal, Nonlinear Filters,Weighted Mean Filters, Median Filters