Speckle noise

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Speckle is a granular 'noise' that inherently exists in and degrades the quality of the active radar, synthetic aperture radar (SAR), medical ultrasound and optical coherence tomography images.

Radar object detection system based on radio waves

Radar is a detection system that uses radio waves to determine the range, angle, or velocity of objects. It can be used to detect aircraft, ships, spacecraft, guided missiles, motor vehicles, weather formations, and terrain. A radar system consists of a transmitter producing electromagnetic waves in the radio or microwaves domain, a transmitting antenna, a receiving antenna and a receiver and processor to determine properties of the object(s). Radio waves from the transmitter reflect off the object and return to the receiver, giving information about the object's location and speed.

Medical ultrasound diagnostic imaging technique

Medical ultrasound is a diagnostic imaging technique based on the application of ultrasound. It is used to create an image of internal body structures such as tendons, muscles, joints, blood vessels, and internal organs. Its aim is often to find a source of a disease or to exclude pathology. The practice of examining pregnant women using ultrasound is called obstetric ultrasound, and was an early development and application of clinical ultrasonography.

Optical coherence tomography

Optical coherence tomography (OCT) is an imaging technique that uses low-coherence light to capture micrometer-resolution, two- and three-dimensional images from within optical scattering media. It is used for medical imaging and industrial nondestructive testing (NDT). Optical coherence tomography is based on low-coherence interferometry, typically employing near-infrared light. The use of relatively long wavelength light allows it to penetrate into the scattering medium. Confocal microscopy, another optical technique, typically penetrates less deeply into the sample but with higher resolution.

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The vast majority of surfaces, synthetic or natural, are extremely rough on the scale of the wavelength. Images obtained from these surfaces by coherent imaging systems such as laser, SAR, and ultrasound suffer from a common phenomenon called speckle. Speckle, in both cases, is primarily due to the interference of the returning wave at the transducer aperture. The origin of this noise is seen if we model our reflectivity function as an array of scatterers. Because of the finite resolution, at any time we are receiving from a distribution of scatterers within the resolution cell. These scattered signals add coherently; that is, they add constructively and destructively depending on the relative phases of each scattered waveform. Speckle noise results from these patterns of constructive and destructive interference shown as bright and dark dots in the image [1]

Speckle noise in conventional radar results from random fluctuations in the return signal from an object that is no bigger than a single image-processing element. It increases the mean grey level of a local area. [2]

Speckle noise in SAR is generally serious, causing difficulties for image interpretation. [2] [3] It is caused by coherent processing of backscattered signals from multiple distributed targets. In SAR oceanography, for example, speckle noise is caused by signals from elementary scatterers, the gravity-capillary ripples, and manifests as a pedestal image, beneath the image of the sea waves. [4] [5]

Capillary wave Wave traveling along the phase boundary of a fluid, whose dynamics and phase velocity are dominated by the effects of surface tension

A capillary wave is a wave traveling along the phase boundary of a fluid, whose dynamics and phase velocity are dominated by the effects of surface tension.

The speckle can also represent some useful information, particularly when it is linked to the laser speckle and to the dynamic speckle phenomenon, where the changes of the speckle pattern, in time, can be a measurement of the surface's activity.

Dynamic speckle

In physics, dynamic speckle is a result of the temporal evolution of a speckle pattern where variations in the scattering elements responsible for the formation of the interference pattern in the static situation produce the changes that are seen in the speckle pattern, where its grains change their intensity as well as their shape along time. One easy to observe example is milk: place some milk in a teaspoon and observe the surface in direct sunlight. You will see a "dancing" pattern of coloured points. Where the milk dries on the spoon at the edge, the speckle is seen to be static. This is direct evidence of the thermal motion of atoms, which cause the Brownian motion of the colloidal particles in the milk, which in turn results in the dynamic speckle visible to the naked eye.

Speckle pattern

A speckle pattern is an intensity pattern produced by the mutual interference of a set of wavefronts. This phenomenon has been investigated by scientists since the time of Newton, but speckles have come into prominence since the invention of the laser and have now found a variety of applications. The term speckle pattern is also commonly used in the experimental mechanics community to describe the pattern of physical speckles on a surface which is useful for measuring displacement fields via digital image correlation.

Speckle Noise Reduction

Several different methods are used to eliminate speckle noise, based upon different mathematical models of the phenomenon. [4] One method, for example, employs multiple-look processing (a.k.a. multi-look processing), averaging out the speckle noise by taking several "looks" at a target in a single radar sweep. [2] [3] The average is the incoherent average of the looks. [3]

A second method involves using adaptive and non-adaptive filters on the signal processing (where adaptive filters adapt their weightings across the image to the speckle level, and non-adaptive filters apply the same weightings uniformly across the entire image). Such filtering also eliminates actual image information as well, in particular high-frequency information, and the applicability of filtering and the choice of filter type involves tradeoffs. Adaptive speckle filtering is better at preserving edges and detail in high-texture areas (such as forests or urban areas). Non-adaptive filtering is simpler to implement, and requires less computational power, however. [2] [3]

There are two forms of non-adaptive speckle filtering: one based on the mean and one based upon the median (within a given rectangular area of pixels in the image). The latter is better at preserving edges whilst eliminating noise spikes, than the former is. There are many forms of adaptive speckle filtering, including the Lee filter, the Frost filter, and the Refined Gamma Maximum-A-Posteriori (RGMAP) filter. They all rely upon three fundamental assumptions in their mathematical models, however: [2]

The Lee filter converts the multiplicative model into an additive one, thereby reducing the problem of dealing with speckle noise to a known tractable case. [6]

Wavelet Analysis

Recently, the use of wavelet transform has led to significant advances in image analysis. The main reason for the use of multiscale processing is the fact that many natural signals, when decomposed into wavelet bases are significantly simplified and can be modeled by known distributions. Besides, wavelet decomposition is able to separate noise and signal at different scales and orientations. Therefore, the original signal at any scale and direction can be recovered and useful details are not lost. [7]

The first multiscale speckle reduction methods were based on the thresholding of detail subband coefficients. [8] Wavelet thresholding methods have some drawbacks: (i) the choice of threshold is made in an ad hoc manner, supposing that signal and noise components obey their known distributions, irrespective of their scale and orientations; and (ii) the thresholding procedure generally results in some artifacts in the denoised image. To address these disadvantages, non-linear estimators, based on Bayes’ theory were developed. [7]

See also

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Synthetic-aperture radar form of radar which is used to create images of an object

Synthetic-aperture radar (SAR) is a form of radar that is used to create two-dimensional images or three-dimensional reconstructions of objects, such as landscapes. SAR uses the motion of the radar antenna over a target region to provide finer spatial resolution than conventional beam-scanning radars. SAR is typically mounted on a moving platform, such as an aircraft or spacecraft, and has its origins in an advanced form of side looking airborne radar (SLAR). The distance the SAR device travels over a target in the time taken for the radar pulses to return to the antenna creates the large synthetic antenna aperture. Typically, the larger the aperture, the higher the image resolution will be, regardless of whether the aperture is physical or synthetic – this allows SAR to create high-resolution images with comparatively small physical antennas.

Discrete wavelet transform

In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information.

Imaging radar application of radar which is used to create two-dimensional images

Imaging radar is an application of radar which is used to create two-dimensional images, typically of landscapes. Imaging radar provides its light to illuminate an area on the ground and take a picture at radio wavelengths. It uses an antenna and digital computer storage to record its images. In a radar image, one can see only the energy that was reflected back towards the radar antenna. The radar moves along a flight path and the area illuminated by the radar, or footprint, is moved along the surface in a swath, building the image as it does so.

Beamforming signal processing technique used in sensor arrays for directional signal transmission or reception

Beamforming or spatial filtering is a signal processing technique used in sensor arrays for directional signal transmission or reception. This is achieved by combining elements in an antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity. The improvement compared with omnidirectional reception/transmission is known as the directivity of the array.

Pulse-Doppler radar radar system

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Polarimetry

Polarimetry is the measurement and interpretation of the polarization of transverse waves, most notably electromagnetic waves, such as radio or light waves. Typically polarimetry is done on electromagnetic waves that have traveled through or have been reflected, refracted or diffracted by some material in order to characterize that object.

Doppler echocardiography

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Salt-and-pepper noise Random noise on digital images

Salt-and-pepper noise is a form of noise sometimes seen on images. It is also known as impulse noise. This noise can be caused by sharp and sudden disturbances in the image signal. It presents itself as sparsely occurring white and black pixels. An effective noise reduction method for this type of noise is a median filter or a morphological filter. For reducing either salt noise or pepper noise, but not both, a contraharmonic mean filter can be effective.

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Contourlets form a multiresolution directional tight frame designed to efficiently approximate images made of smooth regions separated by smooth boundaries. The contourlet transform has a fast implementation based on a Laplacian pyramid decomposition followed by directional filterbanks applied on each bandpass subband.

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Ultrasound computer tomography (USCT), sometimes also Ultrasound computed tomography, Ultrasound computerized tomography or just Ultrasound tomography, is a form of medical ultrasound tomography utilizing ultrasound waves as physical phenomenon for imaging. It is mostly in use for soft tissue medical imaging, especially breast imaging.

Synthetic Aperture Ultrasound (SAU) Imaging is an advanced form of imaging technology used to form high-resolution images in biomedical ultrasound systems. Ultrasound Imaging has become an important and popular medical imaging method, as it is safer and more economical than computer tomography (CT) and magnetic resonance imaging (MRI). Compared with the conventional ultrasound image formation where one transducer or linear array is used, SAU imaging has achieved higher lateral resolution and deeper penetration, which will enable a more accurate diagnosis in medical applications, with no obvious loss in frame rate and without a large burden in computational complexities.

High Resolution Wide Swath (HRWS) imaging is an important branch in Synthetic aperture radar (SAR) imaging, a remote sensing technique capable of providing high resolution images independent of weather conditions and sunlight illumination. This makes SAR very attractive for the systematic observation of dynamic processes on the Earth's surface, which is useful for environmental monitoring, earth resource mapping and military systems.

References

  1. ↑ M. Forouzanfar and H. Abrishami-Moghaddam, Ultrasound Speckle Reduction in the Complex Wavelet Domain, in Principles of Waveform Diversity and Design, M. Wicks, E. Mokole, S. Blunt, R. Schneible, and V. Amuso (eds.), SciTech Publishing, 2010, Section B - Part V: Remote Sensing, pp. 558-77.
  2. 1 2 3 4 5 6 7 8 Brandt Tso & Paul Mather (2009). Classification Methods for Remotely Sensed Data (2nd ed.). CRC Press. pp. 37–38. ISBN   9781420090727.
  3. 1 2 3 4 Giorgio Franceschetti & Riccardo Lanari (1999). Synthetic aperture radar processing. Electronic engineering systems series. CRC Press. pp. 145 et seq. ISBN   9780849378997.
  4. 1 2 Mikhail B. Kanevsky (2008). Radar imaging of the ocean waves. Elsevier. p. 138. ISBN   9780444532091.
  5. ↑ Alexander Ya Pasmurov & Julius S. Zinoviev (2005). Radar imaging and holography. IEE radar, sonar and navigation series. 19. IET. p. 175. ISBN   9780863415029.
  6. ↑ Piero Zamperoni (1995). "Image Enhancement". In Peter W. Hawkes; Benjamin Kazan; Tom Mulvey. Advances in imaging and electron physics. 92. Academic Press. p. 13. ISBN   9780120147342.
  7. 1 2 M. Forouzanfar, H. Abrishami-Moghaddam, and M. Gity, “A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images,” Signal, Image and Video Processing, Springer, vol. 4, pp. 359-75, Sep. 2010
  8. ↑ Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, London (1998)

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