Fabien Chaillan

Speckle noise reduction in SAS imagery

Synthetic aperture sonar (SAS) is actively used in sea bed imagery. Indeed high resolution images provided by SAS are of great interest, especially for the detection, localization and eventually classification of objects lying on sea bed. SAS images are highly corrupted by a granular multiplicative noise, called speckle noise which reduces spatial and radiometric resolutions. The purpose of this article is to present a new adaptive processing that allows image filtering, for both the additive and multiplicative noise case. This new process is based on the marriage between a multi-resolution transformation and a filtering method. The filtering technique used here is based on the two-dimensional stochastic matched filtering method, which maximizes the signal-to-noise ratio after processing and minimizes mean square error between the signals approximation and the original one. Results obtained on real SAS data are presented and compared with those obtained using classical processing.

The Adaptive Stochastic Matched Filter for SAS Images denoising

SAS images are highly corrupted by a granular multiplicative noise, called the speckle which reduces spatial and radiometric resolutions. The purpose of this article is to present a new adaptive processing that allows speckle noise reduction in SAS images, while keeping the spatial resolution. This processing is based on the two-dimensional stochastic matched filtering method with a signal autocorrelation function adaptation and a sub-image size adaptation. Comparison with some classical approaches on real SAS data reveal the efficiency of such an idea

SAS Image De-noising by the Curvelet Stochastic Matched Filter Transform

Over the past few years a great deal of research has been dedicated to SAS image de-noising. Indeed, such images are highly corrupted by a granular multiplicative noise, called the speckle noise, due to the construction of the image itself. This noise reduces spatial and radiometric resolutions. The goal of the research in the area of SAS image de-noising is double: on one hand to reduce the noise level and on the other hand to preserve the spatial resolution. In this context, recently a new approach has been developed: the curvelet transform. Such a method allows good spatial resolution preservation but the results are still slightly disturbed by the introduction of disturbing straight lines due to the process itself. The purpose of this article is to improve the de-noising power of the curvelet transform, while keeping its spatial resolution preservation. To achieve such an objective, the idea is to couple this transform with a filtering technique, known as the stochastic matched filtering method. Results obtained on real SAS data are presented and compared with those obtained using the classical curvelet transform.

Stochastic matched filtering method applied to SAS imagery

SAS (synthetic aperture sonar) has been used in sea bed imagery. Indeed high resolution images provided by SAS are of great interest, especially for the detection localization and eventually classification of objects lying on sea bed. SAS images are highly corrupted by a granular multiplicative noise, called the speckle which reduces spatial and radiometric resolutions. Most of techniques used consist in the use of multilook processing in range, image-domain filters, adaptive filtering or wavelet-domain filtering. The purpose of this article is to present a new adaptive processing that allows image filtering, for both the additive and multiplicative noise case. This processing is based on the two-dimensional stochastic matched filtering method, which maximize the signal to noise ratio after processing and minimize mean square error between the reconstructed signal and the original one. Experimental results on SAS images are presented and compared to those obtained using some classical approaches.

Multiresolution analysis of SAS images

Detection and classification of underwater mines with Synthetic Aperture Sonar (SAS) images is a challenge that can be performed in studying either the echoes or the shadows of mines. However, SAS images present a strong speckle level due to the construction of the image itself. To reduce this speckle level, filtering methods are generally used but all of them strongly deteriorate either the shadow or the echo of the mine. In this article, we propose a new speckle reduction method which allows enhancing jointly mines echoes and shadows. This new process is based on the marriage between a multiresolution transformation and a filtering method. Results obtained on real SAS data are presented and compared with those obtained using classical processing.

An improvement of the discriminating capability of the active sonar by optimization of a criterion based on the Cramer-Rao lower bound

This study is a contribution within the framework of detection of underwater targets using active sonar. This paper aims at proposing an original method allowing a great improvement of the sonar performances. This technique uses a filter, determined by optimizing a criterion based on the Cramer-Rao lower bound. Experimental results are proposed and compared with other approaches.

On the use of the stochastic matched filter for ship wake detection in SAR images

Detecting straight patterns like ship wakes on a SAR image is not easy because there is no a priori information on orientation and position, moreover, SAR images are speckle noised. This article describes a ship wake detection technique based on the discrete Radon transform and stochastic matched filtering (SMF) used in detection. The association of these two processing methods leads to a detection algorithm that only requires the knowledge of the second order statistics of the signal and the noise. Experimentation on real SAR images shows the efficiency of the technique

Chaos Based Random Clock Generator

The integrated analog system proposed in this paper allows to generate a high entropy random clock signal. It is based on the biasing of an oscillator by a random stair-step current. The step amplitude varies randomly on a continuous, uniformly distributed and width-tunable interval. The step length varies randomly on a continuous interval presenting an average adjustable value. The clock signal thus generated presents frequency jumps between random frequencies, occurring at random instants. Its average frequency is tunable and its frequency variation range is continuous, uniformly distributed and width- adjustable. The proposed implementation uses a chaotic oscillator exhibiting a double-scroll attractor as entropy source. It has been simulated from the process parameters of a STMicroelectronics 0.18 mum CMOS technology. It consumes less than 1 mW for a frequency variation range centered on 20 MHz and extending symmetrically on 30 MHz. Its characteristics deviation over process, voltage and temperature (PVT) variations is less than 10%. Its estimated area is approximatively 0.01 mm2.

Coupling the stochastic matched filter and the à Trous algorithm for SAS image de-noising

The wide research domain concerning SAS image de-noising shows the complexity of the problem. SAS devices designed to explore underwater world generate noise-corrupted data, strongly disturbed by the speckle noise, which affect both radiometric and spatial resolutions. Although many de-noising filtering techniques exist in the signal processing society and have shown their efficiency, they suffer of an important restrictive problem: the spatial resolution degradation. The matter is to design a processing having a strong de-noising power while preserving the spatial resolution. To perform this task, we present in this study a new way of SAS image de-noising consisting in coupling an efficient filtering technique, the stochastic matched filtering method, with a multi-resolution analysis technique, the a Trous algorithm. Comparison with some classical approaches on real SAS data reveal the efficiency of such an idea.

Detection of short signal in a noisy underwater environment

Detecting underwater activity is a vast and important challenge. Whether it is for biological or economical reasons it is important to be able to correctly distinguish the many kinds of entities which belong to animal domain or artificial object domain. Most of the time, noisy environment coupled with a modelization problem make underwater detection a difficult problem. In order to avoid a fastidious work consisting on to provide a consistent model of signal of interest, we propose in this study to use the stochastic matched filtering theory in a detection context. As a matter of fact, this processing allows to detect useful information in a noisy environment even with unfavorable signal to noise ratio, under the assumptions that second order statistics of signal of interest and noise are known. Among all the things that should be interesting to detect, we focus on records of clicks of whales in a noisy environment. We show, using this filtering approach in detection, it is possible to detect clicks even with -8 dB unfavorable signal to noise ratio. A comparison with the classical matched filter is done.