Multidimensional empirical wavelet transform

The empirical wavelet transform is a data-driven time-scale representation consisting of adaptive filters. Its robustness to data has made it the subject of intense developments and an increasing number of applications in the last decade. However, it has been mostly studied theoretically for signals so far and its extension to images is limited to a particular mother wavelet. This work presents a general framework for multidimensional empirical wavelet transform from any mother wavelet. In addition, it provides conditions to build wavelet frames for both continuous and discrete transforms.

A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularization-based methods for blind deblurring of QR bar codes in the presence of noise.

A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast, and does not require any parameters. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.