Editorial
- PDF / 691,509 Bytes
- 3 Pages / 600 x 792 pts Page_size
- 59 Downloads / 202 Views
Editorial Giovanni L. Sicuranza Department of Electrical Electronic and Computer Engineering (DEEI), University of Trieste, 34127 Trieste, Italy Email: [email protected]
Gonzalo Arce Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716-3130, USA Email: [email protected]
Moncef Gabbouj Institute of Signal Processing, Tampere University of Technology, 33101 Tampere, Finland Email: [email protected]
Stephen Marshall Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, Scotland Email: [email protected]
This second special issue on nonlinear signal and image processing includes another group of high-quality papers selected among the more than 60 submissions received in response to the EURASIP JASP call for papers. The high number of submissions testifies for the vitality of the field and the great interest existing in the signal processing community for nonlinear theories and tools. The special issue features 18 papers mainly related to the solution of classical problems in the area of nonlinear image and video processing such as noise suppression and image restoration. In addition, contributions in the field of communications together with other interesting applications are considered. The wide range of topics dealt with clearly demonstrates the ubiquitous role played by nonlinear techniques in signal processing tasks. The first group of papers deals with models and techniques for noise estimation and suppression from images. The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. In the paper authored by A. De Stefano et al. three novel and alternative methods for estimating the noise standard deviation are proposed and compared with the MAD method. Using notions from robust statistics, a variational filter referred to as a Huber gradient descent flow is proposed by A. Ben Hamza et al. It is a result of optimizing a Huber functional subject to some noise constraints, and takes
a hybrid form of a total variation diffusion for large gradient magnitudes and of a linear diffusion for small gradient magnitudes. Achieving a good performance in the suppression of impulsive noise is usually at the expense of blurred and distorted image features. One way to avoid this problem is to include a decision-making component in the filtering structure based on effective impulse detection mechanism. The function of the detection mechanism is to check each pixel to detect whether it is distorted or not, and then apply nonlinear filtering only on distorted pixels. E. Bes¸dok proposes an impulse noise removal filter based on an adaptive neuro-fuzzy inference system. The proposed filter comprises three main steps: finding the pixels that are suspected to be corrupted, carrying out Delaunay triangulation, and finally, making estimation for intensity values of corrupted pixels within each of the Delaunay triangles. P. C¸ivicio˘glu et al.
Data Loading...
