Mejdi Trimeche

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

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 disturbances 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 and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.

Motion Blur Identification Based on Differently Exposed Images

In this paper we introduce a new method of motion blur identification that relies on the availability of two, differently exposed, image shots of the same scene. The proposed approach exploits the difference in the degradation models of the two images in order to identify the point spread function (PSF) corresponding to the motion blur, that may affect the longer exposed image shot. The algorithm is demonstrated through a series of experiments that reveal its ability to identify the motion blur PSF even in the presence of heavy degradations of the two observed images.

A spatially adaptive Poissonian image deblurring

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 confidence 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 significantly improves the image restoration quality.

Multichannel image deblurring of raw color components

This paper presents a novel multichannel 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.

Adaptive outlier rejection in image super-resolution

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.

Demosaicing of noisy data: spatially adaptive approach

In this paper we propose a novel color demosaicing algorithm for noisy data. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise which is common for CCD and CMOS digital image sensors. Demosaicing algorithms are used to reconstruct missed red, green, and blue values to produce an RGB image. This is an interpolation problem usually called color filter array interpolation (CFAI). The conventional approach used in image restoration chains for the noisy raw sensor data exploits denoising and CFAI as two independent steps. The denoising step comes first and the CFAI is usually designed to perform on noiseless data. In this paper we propose to integrate the denoising and CFAI into one procedure. Firstly, we compute initial directional interpolated estimates of noisy color intensities. Afterward, these estimates are decorrelated and denoised by the special directional anisotropic adaptive filters. This approach is found to be efficient in order to attenuate both noise and interpolation errors. The exploited denoising technique is based on the local polynomial approximation (LPA). The adaptivity to data is provided by the multiple hypothesis testing called the intersection of confidence intervals (ICI) rule which is applied for adaptive selection of varying scales (window sizes) of LPA. We show the efficiency of the proposed approach in terms of both numerical and visual evaluation.

Enhancing end-user experience in a multi-device ecosystem

A person may use many devices capable of rendering digital content on a regular basis. For instance, the user is in front of a large TV screen, and soon he or she moves away from the TV and wants to continue the media consumption. The transfer of media content across devices needs to be handled seamlessly. In this paper, we present a phone-centric approach to improve the end-user experience in multi-device ecosystem. Together with predefined parameters, we use context information to trigger content adaptation, and generate decisions relating to the transcoding operation. We have made a preliminary UI evaluation of the system, and the system was found to be useful, although requiring fine tuning and further development.

Document image binarization using the camera device in mobile phones

This paper proposes an adaptive binarization method for the document image binarization acquired by an imaging phone. The used algorithm determines the local thresholds with the information from the global trend as well as the local details. As a consequence, the proposed method is good not only for preserving the fine details of the character structure, but also for alleviating noise. The effectiveness of the proposed method is illustrated using the results obtained with a Nokia imaging phone.

Order filters in superresolution image reconstruction

In this paper, we propose the use of order filters in the iterative process of super-resolution reconstruction. At each iteration, order statistic filters are used to filter and fuse the error images. The signal dependent L-filter structure adjusts its coefficients to achieve edge preservation as well as maximum noise suppression in homogeneous regions. Depending on the amount of variance of the image pixels in different directional masks, the filter switches to use the orientation, which is most likely to follow the image edges. This procedure allows for the incorporation of a directional prior across the iterations. The introduction of a spatial filtering stage into the iterative process of super-resolution attempts to increase the robustness towards motion error and image outliers. Experimental results show the improvement obtained on sequences of noisy text images when motion is exactly known, and when a random motion error is introduced to simulate the real life situation of inaccurate motion estimation.

A Method for Simultaneous Outlier Rejection in Image Super-Resolution

In this paper, we propose a method to adaptively reject outlier image regions in the process of super-resolution image reconstruction. We use adaptive FIR filtering while iteratively fusing the gradient images. The LMS adapted filter coefficients automatically isolate the outlier image regions, for which motion was inaccurately estimated. The adaptation criterion used is the median of the errors at each pixel location. Through simulated experiments on synthetic images, we show that the proposed technique performs well in the presence of outlier images. This relatively simple and fast mechanism enables to add robustness in practical implementations of super-resolution, while still effective against Gaussian noise.