Frost Filter Research Articles

Comparison of LULU and Median Filter for Image Denoising

 Abstract—This paper presents a comparison between LULU and Median filters for impulse noise in images. Noise removal from images is always a challenging area of research. Different methods are being used for different image noises such as Wiener filter for Gaussian noise, Frost filter for speckle noise and median filter for impulse noise. LULU filters are widely being used for impulse noise too. LULU filters are nonlinear rank selector operators which are computationally more competent and the performance of the operator is straightforward to describe. The comparison between Median and LULU filters, in this paper, is performed using two different image quality metrics which are RMSE and PSNR.

MRT letter: Quantum noise removal and classification of breast mammogram images

Because of the limitations of the X-ray hardware systems in mammogram machines, the quality of the breast mammogram images may undergo from poor resolution or low contrast. Quantum noise occurs in the mammogram images during acquisition due to low-count X-ray photons. In this work, an adaptive frost filter has been used to remove quantum noise. Local binary patterns have been extracted to classify breast mammograms into benign and malignant using different classifiers. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. Mammographic Institute Society Analysis database of mammography has been used for experimentation. Peak signal-to-noise ratio and structural similarity index measure are used to test the validity of adaptive frost filter. Experiment results show that proposed technique produces better results.

EVALUATION OF FILTERS FOR ENVISAT ASAR SPECKLE SUPPRESSION IN PASTURE AREA

Abstract. In order to quantify real time pasture biomass from SAR image, regression model between ground measurements of biomass and ENVISAT ASAR backscattering coefficient should be built up. An important prerequisite of valid and accurate regression model is accurate grass backscattering coefficient which, however, cannot be obtained when there is speckle. Speckle noise is the best known problem of SAR images because of the coherent nature of radar illumination imaging system. This study aims to choose better adaptive filter from NEST software to reduce speckle noise in homogeneous pasture area, with little regard to linear feature (e.g. edge between pasture and forest) or point feature (e.g. pond, tree) preservation. This paper presents the speckle suppression result of ENVISAT ASAR VV/VH images in pasture of Western Australia (WA) using four built-in adaptive filters of the NEST software: Frost, Gamma Map, Lee, and Refined Lee filter. Two indices are usually used for evaluation of speckle suppression ability: ENL (Equivalent Number of Looks) and SSI (Speckle Suppression Index). These two, however, are not reliable because sometimes they overestimate mean value. Therefore, apart from ENL and SSI, the authors also used a new index SMPI (Speckle Suppression and Mean Preservation Index). It was found that, Lee filter with window size 7×7 and Frost filter (damping factor = 2) with window size 5×5 gave the best performance for VV and VH polarization, respectively. The filtering, together with radiometric calibration and terrain correction, paves the way to extraction of accurate backscattering coefficient of grass in homogeneous pasture area in WA.

A non-linear complex diffusion based filter adapted to Rayleigh's speckle noise for de-speckling ultrasound images

In this paper, an edge and structure preserving non-linear complex diffusion based filter adapted to Rayleigh’s speckle noise for speckle reduction from 2D ultrasound images is proposed and implemented in MATLAB 7.0. The initial condition of the proposed filter is the speckle noised ultrasound image and the speckle-reduced image is obtained after certain iterations of the filter till its convergence. For digital implementations, the proposed scheme has been discretised using the finite difference scheme. The performance of the proposed complex diffusion-based filter has been evaluated both qualitatively and quantitatively. A comparative study of the proposed scheme with other standard speckle reduction schemes such as the homomorphic Wiener filter, the Lee filter, the Frost filter, the Kuan filter and the Speckle Reducing Anisotropic Diffusion (SRAD) filter for several ultrasound images with varying amounts of speckle noise variance is also presented. The obtained results show that the proposed non-linear complex diffusion-based scheme performs better than all other schemes in consideration and is also well capable of preserving edges and fine structures from ultrasound images during speckle reduction.

A fourth-order PDE-based non-linear filter for speckle reduction from Optical Coherence Tomography images

In this paper, a fourth-order PDE-based non-linear filter for speckle reduction from Optical Coherence Tomography (OCT) images is proposed in a variational framework. The speckle noise pattern in OCT images is distributed according to Rayleigh’s Probability Distribution Function (pdf). The initial condition of the proposed PDE-based filter is the speckle noised OCT images and the output is the better quality speckle reduced image. For digital implementations, the proposed scheme is discretised using the finite difference scheme. The performance comparison of the proposed scheme with other standard speckle reduction techniques such as the Lee filter, the Frost filter, the Kuan filter, the SRAD filter, the homomorphic Wiener filter and homomorphic versions of the anisotropic diffusion based filter, the non-linear complex diffusion-based filter and the fourth order PDE-based filter is also presented. The obtained results justify the applicability of the proposed scheme.

Pre-processing of TerraSAR-X Data for Speckle Removal: An Approach for Performance Evaluation

Measures to select despeckling filter to remove speckles from TerraSAR-X image have been evaluated for an area having varied land cover classes. Gamma, Lee, Sigma, Mean, Median and Frost filters were applied in different window sizes. These images are visually compared and four parameters viz. Noise Mean Value (NMV), Noise Variance (NV), Mean Square Difference (MSD) and Equivalent Numbers of Looks (ENL) are calculated. These parameters were well implemented to compare results of various despeckling filters on TerraSAR-X image recorded in high resolution spot mode and checked for the constraints. Median 7x7 filter was found most suitable among other filters to despeckle the images.

FPGA-Based Adaptive Speckle Suppression Filter for Underwater Imaging Sonar

Underwater forward-looking imaging sonar (FLS) is widely used on stationary and moving platforms to overcome underwater visibility problems. The SONAR images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. Speckle reduction filters are necessary to optimize the images' exploitation procedures. The results of speckle filters may vary from one sensor and one wavelength to another; therefore, no generic de-speckling algorithm exists. Several studies have been carried out on speckle noise suppression on sidescan sonar, but the problem of speckle noise suppression for FLS has not yet been covered. A comparison of the most used classical speckle suppression filters as well as advanced wavelet-based ones was carried out. The Frost filter was found to be the most adequate for FLS data, but also the most computationally complex and not suitable for real-time processing. Two novel architectures for real-time and low-power field-programmable gate array (FPGA) implementation of the Frost speckle filter for underwater imaging sonar are presented. The proposed architectures have superior performance and power efficiency compared to standard software implementation.

Edge-detection-based filter for SAR speckle noise reduction

Some adaptive filters, such as the Kuan, Lee, minimum mean square error (MMSE) and Frost filters, have been tested on synthetic aperture radar (SAR) data without considering the level of homogeneity in the pixels. Therefore, they degrade the spatial resolution of images and smooth details, while also decreasing the speckle noise level. There are other filters, such as the enhanced Lee and gamma maximum a posteriori (MAP), that utilize the level of homogeneity, but they cannot adequately suppress speckle noise. In addition to these weaknesses, pixels surrounding a point scatterer are also treated as point scatterers due to inadequacy of the method based on evaluating the coefficient of variation for differentiating between them and the point scatterer. We have developed a new method based on the assessment of similarity of homogeneity levels in the image, incorporating edge-detection filters to identify meaningful features and an algorithm to filter the pixels surrounding point scatterers. This method, called the UNSW (University of New South Wales) adaptive filter (UAF), was compared to nine filters using different quantitative and qualitative methods. The results show the ability of the UAF to simultaneously reduce speckle and preserve details as well as its ability to filter more pixels. The effect of increasing the damping factor on speckle noise reduction performance has also been assessed using this method.

Despeckling of Oil Spill SAR Images using Fusion Technique

speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the oil spill images and makes interpretations, analysis and classifications of SAR images harder. Therefore, some speckle mitigation is necessary prior to the processing of SAR oil spill images. In this paper, some basic speckle reduction filters like Kuan, Lee, Frost, antistrophic diffusion and SRAD filters are used. A New method is proposed for despeckling of SAR oil spill images which combines the frost filter with relaxed median filter. The proposed method gives better results when compared to other methods in terms of statistical parameters like PSNR, MSE, energy and entropy value. The approach can also reduce the computing time compared with other approaches.

An adaptive non-linear PDE-based speckle reduction technique for ultrasound images

In this paper, an adaptive nonlinear complex diffusion based filter is proposed to reduce the speckle noise from ultrasound images. The adaptive and modified version of a fourth order PDE is also proposed and examined for its suitability and efficacy for speckle reduction from ultrasound images. The performance of the proposed method is compared with constraint driven anisotropic diffusion based method, modified fourth order PDE based method and other methods such as Speckle Reducing Anisotropic Diffusion (SRAD) filter, Lee filter, Frost filter and Kuan filter in terms of various performance metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Correlation Parameter (CP) and Mean Structure Similarity Index Map (MSSIM). The obtained results justify the applicability of the proposed scheme.

Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images

In this paper, the partial differential equation (PDE) based homomorphic filtering technique is proposed for speckle reduction from digitally reconstructed holographic images based on the concepts of complex diffusion processes. For digital implementations, the proposed scheme was discretized using finite differences scheme. Further, the performance of the proposed PDE-based technique is compared with other speckle reduction techniques such as homomorphic anisotropic diffusion filter based on extended concept of Perona and Malik (1990) [2], homomorphic Weiner filter, Lee filter, Frost filter, Kuan filter, speckle reducing anisotropic diffusion (SRAD) filter and hybrid filter in the context of digital holography. For the comparison of various speckle reduction techniques, the performance is evaluated quantitatively in terms of all possible parameters that justify the applicability of a scheme for a specific application. The chosen parameters are mean-square-error (MSE), normalized mean-square-error (NMSE), peak signal-to-noise ratio (PSNR), speckle index, average signal-to-noise ratio (SNR), effective number of looks (ENL), correlation parameter (CP), mean structure similarity index map (MSSIM) and execution time in seconds. For experimentation and computer simulation MATLAB 7.0 has been used and the performance is evaluated and tested for various sample holographic images for varying amount of speckle variance. The results obtained justify the applicability of proposed schemes.

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.

A model for removal of speckle noise in SAR images (ALOS PALSAR)

Speckle noise is primarily due to the phase fluctuations of the electromagnetic return signals. Since inherent spatial-correlation characteristics of speckle in synthetic aperture radar (SAR) images are not exploited in existing multiplicative models for speckle noise, a speckle noise model is proposed here that provides a new framework for modelling and reducing the speckle noise. Both quantitative and qualitative criteria, including speckle reduction and texture preservation, are used to evaluate the performance of the proposed filter; one PALSAR (new Japanese sensor) image and a JERS-1 image are employed in the evaluation. The results showed that the proposed filter is slightly better than commonly used filters such as the Kuan, gamma, enhanced Lee, and enhanced Frost filters. The proposed filter can be used in different applications, including mapping and forestry biomass estimation. Furthermore, one of the benefits of the proposed filter is that it is independent of the threshold, which is required in most commonly used filters. The proposed filter was tested with SAR images of different sites in the northern forests of Iran.

Coral reef detection using SAR/RADARSAT-1 images at Costa dos Corais, PE/AL, Brazil

The present work aimed to examine the potentials of SAR RADARSAT-1 images to detect emergent coral reefs at the Environmental Protection Area of "Costa dos Corais". Multi-view filters were applied and tested for speckle noise reduction. A digital unsupervised classification based on image segmentation was performed and the classification accuracy was evaluated by an error matrix built between the SAR image classification and a reference map obtained from a TM Landsat-5 classification. The adaptative filters showed the best results for speckle suppression and border preservation, especially the Kuan, Gamma MAP, Lee, Frost and Enhanced Frost filters. Small similarity and area thresholds (5 and 10, respectively) were used for the image segmentation due to the reduced dimensions and the narrow and elongated forms of the reefs. The classification threshold of 99% had a better user's accuracy, but a lower producer's accuracy because it is a more restrictive threshold; therefore, it may be possible that it had a greater omission on reef classification. The results indicate that SAR images have a good potential for the detection of emergent coral reefs.

Snow wetness mapping using advanced synthetic aperture radar data

The main objective of the study is to estimate snow wetness using advanced synthetic aperture radar alternative polarization single look complex data. A software has been developed for backscattering coefficient image generation and advanced synthetic aperture radar, single look complex data have been processed for backscattering coefficient image generation for dual (HH and VV) polarization data. Incidence angle image was extracted from the advanced synthetic aperture radar header data using interpolation method. These mages were multi-looked 5 times in azimuth and 1 time in range direction. Multi-looked backscattering image was despeckled using Frost filter technique. Advanced synthetic aperture radar backscattering coefficient image has been calibrated and processed into terrain corrected image in Universal Transverse Mercator (UTM), zone 43 north and WGS-84 datum map projection using ERDAS imagine software. In this study, we have used an integral equation method based inversion model for snow wetness estimation. The combined correlation coefficient between measured and estimated snow wetness was observed to be 0.89 at 95% confidence interval at Dhundi observatory and individual correlation coefficient between both measurements were observed as 0.78, 0.68,0.6 and 0.66 for 24th Feb., 27th Feb., 4th March, and 7th March, 2006 respectively at 95% confidence interval and average absolute error 2.52%. The snow wetness range varies from 0-15% by volume.

Advantages of Adaptive Speckle Filtering Prior to Application of Iterative Deconvolution Methods for Optical Coherent Tomography Imaging

The axial resolution of optical coherence tomography (OCT) is closely related to the light source spectrum width. Unfortunately, most basic sources providing the required power for decent OCT image have narrow spectra, which generate a resolution loss. Assuming the OCT system is linear shift-invariant, we can consider the system output as the convolution of the theoretical signal with a system impulse response due to this spectrum narrowness. It becomes then possible to enhance this resolution through iterative deconvolution methods (IDM). However those methods have a significant drawback, as they usually significantly enhance speckle, which is another consequence of the source spectrum narrowness. To compensate this, we rely on preliminary speckle filtering, and especially the adaptative ones, which allow tackling the speckle without blurring the image. We first studied enhancement proposed by most popular IDMs on OCT images, and then the effect of preliminary adaptive speckle filtering (ASF) by different common adaptive methods. Then, among those methods, we combined Frost filter and Richardson-Lucy deconvolution in the appropriated way; this way we both enhanced resolution by 2 and reduced speckle (raising CNR from 0.7 to 1.3)

Road vectors update using SAR imagery: a snake-based method

The paper presents an approach for roads detection based on synthetic aperture radar (SAR) images and road databases. The vectors provided by the database are refined using active contours (snakes). In this framework, we firstly develop a restoration filter based on the frost filter achieving an acceptable compromise between speckle elimination and lines preserving. This is followed by a line plausibility calculation step which is used to deform the snake from its initial location toward the final solution. The snake is reformulated using finite elements method. The setting of the snake parameters is not an obvious problem especially when they are tuned by trial-and-error process. We propose a new automatic computational rule for the snake parameters. Our approach is validated by a series of tests on synthetic and SAR images.

Speckle reducing anisotropic diffusion

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee and Frost filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

Toward edge sharpening: a SAR speckle filtering algorithm

This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening; since SAR sensors have limited bandwidths, leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points. Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original. Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms. Possible distortions caused by filters are explained.

Adaptive filtering of random noise in 2-D geophysical data

Random noise is often a problem in geophysical data visualization because it obscures fine details and complicates identification of image features. Adaptive filters have recently been used to suppress speckle (random) noise in synthetic aperture radar (SAR) images. SAR data are similar to seismic reflection data, both in their data acquisition approach and in their final data processed format. The nature of the random noise associated is also very similar, and adaptive filters can be applied to reduce random noise in both types of data sets. In this paper several popular adaptive filters—the Lee filter, the Frost filter, and the Kuan filter, which have been used frequently for speckle reduction in SAR data—are tested on Lithoprobe (AGT) deep seismic reflection data and on one set of oil industry shallow seismic reflection data. In addition, a standard band‐pass filter, which is common in many seismic data processing packages, is tested with the oil industry test data. Performance of the adaptive filters is also tested on Radarsat SAR data. The random (speckle) noise in both the Lithoprobe and the Radarsat (SAR) data sets is statistically very similar, and the adaptive filters tested successfully suppressed random noise while minimizing blurring. Among the tested filters the enhanced Lee filter performed best, closely followed by the enhanced Frost filter and the Kuan filter. The background noise in the oil industry seismic data is statistically quite different from the above two data sets; the results obtained were less than satifactory, although they were still encouraging. With the oil industry data, the enhanced Frost and Kuan filters performed better than the enhanced Lee filter. The commonly used band‐pass filter successfully removed the background (random) noise, but it also suppressed the reflection events, making the final result less desirable.