Radu Ciprian Bilcu

A transform domain LMS adaptive filter with variable step-size

We introduce a new transform domain (least mean square) LMS algorithm with variable step. The existing approaches use different time-variable step-sizes for each filter tap. The step-sizes are time-variable due to the power estimates of each transform coefficient. In our new approach, for each step-size we define a local component that is given by the power normalization, and a global component that is the same for each filter coefficient. We show that if the global component is also made time-variable, depending on the output error, the speed of convergence can be significantly improved.

A new variable length LMS algorithm: theoretical analysis and implementations

This paper addresses the problem of finding the optimum length for the adaptive least mean square (LMS) filter. In almost all papers published in this field, the length of the adaptive filter is maintained constant and the values of the coefficients are modified such that the output mean squared error (MSE) is minimized. There are some practical applications where we need to have information about the length of the optimum Wiener solution. As an example in system identification, one needs to have not only accurate approximation of the coefficient values but also the number of the coefficients of the unknown system. Here we provide the theoretical analysis of the LMS algorithm where the length mismatch between the adaptive filter and the unknown filter is taken into account. Based on this theoretical analysis a new variable length LMS algorithm is introduced.

High dynamic range imaging on mobile devices

Natural scenes usually produce radiance maps that have a dynamic range much larger than the dynamic range of the imaging sensors. Due to this fact the captured images, almost always, contain under-exposed and saturated regions. Among several solutions, proposed in the open literature, the multi-frame approaches have been shown to produce high quality results by combining several shots of the same scene, captured at different exposure times. Here we introduce a low complexity multi-frame approach suitable for mobile implementations. We have implemented our method in Symbian OS in a Nokia cameraphone and the results obtained with our proposed system are shown in the paper.

Fast nonlocal means for image denoising

Due to camera module miniaturization, the pixel area of the digital sensors decreases which decreases also the signal to noise ratio in the captured images. As a consequence, image de-noising is still an important topic in digital image processing field. In this paper we address the problem of image de-noising using the nonlocal means algorithm. This method has excellent de-noising properties but at the expense of increasing the computational complexity. We propose here a novel approach that provides similar filtering capabilities with much less computational effort and shorter processing time. Our proposed algorithm is compared with the nonlocal means algorithm and with another fast implementation, recently reported, in terms of processing time and noise reduction capability (from both visual impression and mean squared error points of view). The comparative results are presented for artificially degraded images and also for images obtained with a camera phone.

Audio based solutions for detecting intruders in wild areas

This paper presents an overview of the work that has been done in the field of wildlife intruder detection using only acoustic sensors. The motivation of such an application is related to protection of large wildlife regions, such as forests, lakes, and other natural reservations. The sounds of interest are represented by humans, engines, birds and animals. In order to simulate various environmental situations, different types of noisy environments have been considered. Both low complexity and standard audio classification methods are presented. Standard audio classification methods prove to be more robust, but at an expense of significantly increased complexity. Since low complexity systems are more feasible for monitoring remote areas, the complexity issue is discussed and solutions are proposed.

Denoising and interpolation of noisy Bayer data with adaptive cross-color filters

We propose a novel approach for joint denoising and interpolation of noisy Bayer-patterned data acquired from a digital imaging sensor (e.g., CMOS, CCD). The aim is to obtain a full-resolution RGB noiseless image. The proposed technique is specifically targeted to filter signal-dependant, e.g. Poissonian, or heteroscedastic noise, and effectively exploits the correlation between the different color channels. The joint technique for denoising and interpolation is based on the concept of local polynomial approximation (LPA) and intersection of confidence intervals (ICI). These directional filters utilize simultaneously the green, red, and blue color channels. This is achieved by a linear combination of complementary-supported smoothing and derivative kernels designed for the Bayer data grid. With these filters, the denoised and the interpolated estimates are obtained by convolutions over the Bayer data. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional filter. Fusing estimates from multiple directions provides the final anisotropic denoised and interpolated values. The full-size RGB image is obtained by placing these values into the corresponding positions in the image grid. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real camera data.

Constrained Unsharp Masking for Image Enhancement

In this paper we present a cost-effective solution for combined de-noising and sharpening of digital images. Our method combines the unsharp masking and sigma filtering techniques through a regularization mechanism thus ensuring effective noise reduction and edge enhancement in the processed image. We describe our method in detail and we analyze the proposed implementation through extensive experiments done in various scenarios. Due to its low computational complexity the proposed method is well suited for mobile implementations.

Wildlife intruder detection using sounds captured by acoustic sensors

In this paper we classify the sounds originated from humans, birds and cars. The motivation of such a classification is to detect the intruders into protected wildlife regions such as protected forests, lakes, and other natural reservations. The proposed algorithm for sound encoding and classification is Time Encoded Signal Processing and Recognition (TESPAR) combined with the archetypes technique. We have tested our method on a database consisting of 300 recordings, 100 for each class, and several types of noise (white Gaussian noise, rain sound and wind sound) have been added to the recordings in order to simulate the different outdoor environments. Several pre-processing steps have been included and tested in order to verify the improvement of the classification accuracy. We performed a downsampling from 8 kHz to 6 kHz of the original recordings, followed by band pass filtering and the results shown an increased efficiency of TESPAR in the classification process.

Variable step-size LMS adaptive filters for CDMA multiuser detection

Due to its simplicity the adaptive least mean square (LMS) algorithm is widely used in code-division multiple access (CDMA) detectors. However its convergence speed is highly dependent on the eigenvalue spread of the input covariance matrix. For highly correlated inputs the LMS algorithm has a slow convergence which require long training sequences and therefore low transmission speeds. Another drawback of the LMS is the trade-off between convergence speed and steady-state error since both are controlled by the same parameter, the step-size. In order to eliminate these drawbacks, the class of variable step-size LMS (VSSLMS) algorithms was introduced. In this paper, we study the behavior of some algorithms belonging to the class of VSSLMS for training based multiuser detection in a CDMA system. We show that the proposed complementary pair variable step-size LMS algorithms highly increase the speed of convergence while reducing the trade-off between the convergence speed and the output error.

Multiframe Auto White Balance

Automatic white balancing has the aim of ensuring color consistency of the captured image over a wide range of light sources with different color temperatures. Almost all available automatic white balance techniques contain two main processing steps. Firstly, the illuminant is identified and some gaining coefficients are computed for each color component. After that, the red, green, and blue color components are multiplied with the corresponding gains to obtain the result image. The difference between the majority of the existing solutions rely on the method for gain computation. In this paper we deal with the second step of the AWB procedure where we expose differently each color component instead of multiplying it with a constant.