Kernel

density estimation with adaptive varying window siz

SELECTION OF VARYING SPATIALLY ADAPTIVE REGULARIZATION PARAMETER FOR IMAGE DECONVOLUTION

The deconvolution in image processing is an inverse illposed problem which necessitates a trade-off between-delity to data and smoothness of a solution adjusted by a regularization parameter. In this paper we propose two techniques for selection of a varying regularization parameter minimizing the mean squared error for every pixel of the image. The rst algorithm uses the estimate of the squared point-wise bias of the regularized inverse. The second algorithm is based on direct multiple statistical hypothesis testing for the estimates calculated with different regularization parameters. The simulation results on images illustrate the ef ciency of the proposed technique

Multichannel Image Deblurring of Raw Color Components

This paper presents a novel multi-channel image restoration algorithm. The main idea is to develop practical approaches to reduce optical blur from noisy observations produced by the sensor of a camera phone. An iterative deconvolution is applied separately to each color channel directly on the raw data. We use a modified iterative Landweber algorithm combined with an adaptive denoising technique. The adaptive denoising is based on local polynomial approximation (LPA) operating on data windows selected by the rule of intersection of confidence intervals (ICI). In order to avoid false coloring due to independent component filtering in the RGB space, we have integrated a novel saturation control mechanism that smoothly attenuates the high-pass filtering near saturated regions. It is shown by simulations that the proposed filtering is robust with respect to errors in point-spread function and approximated noise models. Experimental results show that the proposed processing technique produces significant improvement in perceived image resolution

Color Filter Array Interpolation Based on Spatial Adaptivity

Conventional approach in single-chip digital cameras is a use of color lter arrays (CFA) in order to sample di erent spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, in order to produce an RGB image. In this paper we propose a novel demosaicing algorithm for the Bayer CFA. For the algorithm design we assume that the initial interpolation estimates of color channels contain two additive components: the true values of color intensities and the errors. The errors are considered as an additive noise, and often called as a demosaicing noise, that has to be removed. This noise is not white and strongly depends on the signal. Usually, the intensity of this noise is higher near edges of image details. We use specially designed signal-adaptive lter to remove the interpolation errors. This lter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of con dence intervals (ICI) applied for selection adaptively varying scales (window sizes) of LPA. The LPA-ICI technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The e ciency of the proposed approach is demonstrated by simulation results