Abstract-We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbancies in the output data. We further explicitly take into account the clipping of the data (over-and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images as well as with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.
A spatially adaptive image deblurring algorithm is presented for Poisson observations. It adapts to the unknown image smoothness by using local polynomial approximation (LPA) kernel estimates of varying scale and direction based on the intersection of conÞ-dence intervals (ICI) rule. The signal-dependant characteristics of the Poissonian noise are exploited to accurately compute the pointwise variances of the directional estimates. The results show that this accurate pointwise adaptive algorithm signiÞcantly improves the image restoration quality.
One critical aspect to achieve efficient implementations of image super-resolution is the need for accurate subpixel registration of the input images. The overall performance of super-resolution algorithms is particularly degraded in the presence of persistent outliers, for which registration has failed. To enhance the robustness of processing against this problem, we propose in this paper an integrated adaptive filtering method to reject the outlier image regions. In the process of combining the gradient images due to each low-resolution image, we use adaptive FIR filtering. The coefficients of the FIR filter are updated using the LMS algorithm, which automatically isolates the outlier image regions by decreasing the corresponding coefficients. The adaptation criterion of the LMS estimator is the error between the median of the samples from the LR images and the output of the FIR filter. Through simulated experiments on synthetic images and on real camera images, we show that the proposed technique performs well in the presence of motion outliers. This relatively simple and fast mechanism enables to add robustness in practical implementations of image super-resolution, while still being effective against Gaussian noise in the image formation model.
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.
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