Sakari Alenius

Noise Measurement for Raw-Data of Digital Imaging Sensors by Automatic Segmentation of Nonuniform Targets

In this paper, we present a new method for measuring the temporal noise in the raw-data of digital imaging sensors [e.g., CMOS and charge-coupled device (CCD)]. The method is specially designed to estimate the variance function which describes the signal-dependent noise found in raw-data. It gives the standard-deviation of the noise as a function of the expectation of the pixel raw-data output value. In contrast with established methods (such as the ISO 15739), our method does not require the use of a specific target or a particular calibration. This is possible due to an automatic segmentation embedded in the data analysis. We show experimental results for the raw-data from two different CMOS sensors of commercial cameraphones.

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.

Method of Motion Estimation for Image Stabilization

In this paper we introduce a novel approach to global motion estimation for image stabilization application. The method is robust to image degradations characteristic to image stabilization, e.g. image blur caused by motion or out of focus. In addition, due to its low computational complexity, the proposed method could be included in a real-time digital image stabilization system. The ability of the proposed registration approach to capture the global motion of the camera in the presence of image degradations and outliers, have been evaluated through a large number of experiments. The results reveal that, in spite of its lower computational complexity, the proposed method achieves sub-pixel motion estimation accuracy, close to the performance achieved by the state of the art approaches to image registration

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.

A novel method for multi-focus image fusion

Digital cameras often suffer from limited depth-of-field which results in blurring parts of the captured image. While, in some applications, partial blurring of the scene might be desired, capturing all-in-focus images is usually of main interest. In this paper we describe a method for multi-focus image fusion able to provide a sharp image from a set of several images captured with different focus settings. Our proposed method is presented in detail and experimental results are shown comparing our approach with other similar methods from the open literature in terms of processing time, visual quality and root mean squared error.

Camera assisted multimodal user interaction

Since more processing power, new sensing and display technologies are already available in mobile devices, there has been increased interest in building systems to communicate via different modalities such as speech, gesture, expression, and touch. In context identification based user interfaces, these independent modalities are combined to create new ways how the users interact with hand-helds. While these are unlikely to completely replace traditional interfaces, they will considerably enrich and improve the user experience and task performance. We demonstrate a set of novel user interface concepts that rely on built-in multiple sensors of modern mobile devices for recognizing the context and sequences of actions. In particular, we use the camera to detect whether the user is watching the device, for instance, to make the decision to turn on the display backlight. In our approach the motion sensors are first employed for detecting the handling of the device. Then, based on ambient illumination information provided by a light sensor, the cameras are turned on. The frontal camera is used for face detection, while the back camera provides for supplemental contextual information. The subsequent applications triggered by the context can be, for example, image capturing, or bar code reading.

Mutual information refinement for flash-no-flash image alignment

Flash-no-flash imaging aims to combine ambient light images with details available in flash images. Flash can alter color intensities radically leading to changes in gradient directions and strengths, as well as natural shadows possibly being removed and new ones created. This makes flash-no-flash image pair alignment a challenging problem. In this paper, we present a new image registration method utilizing mutual information driven point matching accuracy refinement. For a phase correlation based method, accuracy improvement through the suggested point refinement was over 40 %. The new method also performed better than the reference methods SIFT and SURF by 3.0 and 9.1 % respectively in alignment accuracy. Visual inspection also confirmed that in several cases the proposed method succeeded in registering flash-no-flash image pairs where the tested reference methods failed.

Combined de-noising and sharpening of color images in DCT domain

In this paper we present a new DCT-based method for combined de-noising and sharpening of color images. Our solution combines alpha-rooting and thresholding the DCT coefficients to achieve both sharpening and noise reduction. In our approach, sharpening and de-noising are done in the YUV color space with Y, U and V components being processed differently. Moreover, the size of the sliding DCT transform is variable and adaptive to the local characteristics of the input image thus increasing the visual quality of the processed image. We describe our method in detail and we present experimental results, performed on color images, to compare its performance with other existing solutions.

Landweber image restoration with adaptive step-size

The Landweber algorithm is probably one of the simplest iterative method for image restoration which makes it a good choice for practical implementations. In this paper, we introduce a novel Landweber method that uses an adaptive step-size to improve the convergence speed and ensure stability. We review the convergence behavior of the Landweber algorithm and we introduce an enhanced method which we compare with an existing approach that also uses time-varying step-size.