Ant colony optimisation-based radiation pattern manipulation algorithm for electronically steerable array radiator antennas

A new algorithm for manipulating the radiation pattern of Electronically Steerable Array Radiator Antennas is proposed. A continuous implementation of the Ant Colony Optimisation (ACO) technique calculates the optimal impedance values of reactances loading different parasitic radiators placed in a circle around a centre antenna. By proposing a method to obtain a suitable sampling frequency of the radiation pattern for use in the optimisation algorithm and by transforming the reactance search space into the search space of associated phases, special care was taken to create a fast and reliable implementation, resulting in an approach that is suitable for real-time implementation. The authors compare their approach to analytical techniques and optimisation algorithms for calculating these reactances. Results show that the method is able to calculate near-optimal solutions for gain optimisation and side lobe reduction

Robust active contour segmentation with an efficient global optimizer

Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed, combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information, but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for the proposed active contour method. It is experimentally shown that the proposed method has significant better results in the presence of noise and clutter

A recursive scheme for computing autocorrelation functions of decimated complex wavelet subbands

This paper deals with the problem of the exact computation of the autocorrelation function of a real or complex discrete wavelet subband of a signal, when the autocorrelation function (or Power Spectral Density, PSD) of the signal in the time domain (or spatial domain) is either known or estimated using a separate technique. The solution to this problem allows us to couple time domain noise estimation techniques to wavelet domain denoising algorithms, which is crucial for the development of blind wavelet-based denoising techniques. Specifically, we investigate the Dual-Tree complex wavelet transform (DT-CWT), which has a good directional selectivity in 2-D and 3-D, is approximately shift-invariant, and yields better denoising results than a discrete wavelet transform (DWT). The proposed scheme gives an analytical relationship between the PSD of the input signal/image and the PSD of each individual real/complex wavelet subband which is very useful for future developments. We also show that a more general technique, that relies on Monte-Carlo simulations, requires a large number of input samples for a reliable estimate, while the proposed technique does not suffer from this problem

Variational multi-image stereo matching

In two-view stereo matching, the disparity of occluded pixels cannot accurately be estimated directly: it needs to be inferred through, e.g., regularisation. When capturing scenes using a plenoptic camera or a camera dolly on a track, more than two input images are available, and - contrary to the two-view case -pixels in the central view will only very rarely be occluded in all of the other views. By explicitly handling occlusions, we can limit the depth estimation of pixel (P) over right arrow to only use those cameras that actually observe (p) over right arrow. We do this by extending variational stereo matching to multiple views, and by explicitly handling occlusion on a view-by-view basis. Resulting depth maps are illustrated to be sharper and less noisy than typical recent techniques working on light fields

Reconstruction of high dynamic range images with poisson noise modeling and integrated denoising

In this paper, we present a new method for High Dynamic Range (HDR) reconstruction based on a set of multiple photographs with different exposure times. While most existing techniques take a deterministic approach by assuming that the acquired low dynamic range (LDR) images are noise-free, we explicitly model the photon arrival process by assuming sensor data corrupted by Poisson noise. Taking the noise characteristics of the sensor data into account leads to a more robust way to estimate the non-parametric camera response function (CRF) compared to existing techniques. To further improve the HDR reconstruction, we adopt the split-Bregman framework and use Total Variation for regularization. Experimental results on real camera images and ground-truth data show the effectiveness of the proposed approach

Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain

In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. ABSTRACT In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU