Spatially Adaptive 3D Inverse for Optical Sectioning

In this paper, we propose a novel nonparametric approach to reconstruction of three-dimensional (3D) objects from 2D blurred and noisy observations which is a problem of computational optical sectioning. This approach is based on an approximate image formation model which takes into account depth varying nature of blur described by a matrix of shift-invariant 2D point-spread functions (PSF) of an optical system. The proposed restoration scheme incorporates the matrix regularized inverse and matrix regularized Wiener inverse algorithms in combination with a novel spatially adaptive denoising. This technique is based on special statistical rules for selection of the adaptive size and shape neighbourhood used for the local polynomial approximation of the 2D image intensity. The simulations on a phantom 3D object show efficiency of the developed approach. The objective result evaluation is presented in terms of quadratic-error criteria

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

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