Spatial Domain Filtering Research Articles

Despeckling of medical ultrasound kidney images in the curvelet domain using diffusion filtering and MAP estimation

Speckle remains as a fundamental problem in all coherent imaging modalities such as ultrasound, synthetic aperture radar (SAR) and laser. Presence of speckle gives a granular appearance to the image, hence hinders the details of the underlying object. This paper presents a novel speckle reduction method in the curvelet domain with coefficient modelling and diffusion filtering of the coefficients. An un-decimated Άtrous based curvelet transform of the image is done. A shrinkage function is estimated with the curvelet transformed coefficients using Maximum A posteriori Probability (MAP) technique with the priori distribution assumption as Gaussian. Part of the curvelet coefficients are diffusion filtered using Perona Malik Anisotrpic Diffusion filter (PMAD) and the rest of the coefficients are modelled using the estimated shrinkage function. Proposed algorithm has been tested with the synthetic as well as real time ultrasound kidney images and is found to be effective in removing the speckles. The proposed system is compared with the popular spatial domain filters such as Frost, Kuan and Wiener, also with wavelet and curvelet domain methods. Objective evaluation using peak signal to noise ratio (PSNR), coefficient of correlation (CoC) and structural similarity (SSIM) measures have been done to demonstrate the effectiveness of the proposed system.

Image Restoration - A Survey

Image restoration is the process of restoring the degraded or corrupted image back to its original form. It is the initial step of image processing. Noise is added in the image while sending an image from one place to another via satellites, wireless, or during image acquisition process. There are various types of noises such as salt and pepper noise(impulse noise), Gaussian noise etc. The main goal of image restoration is to recover or improve the quality of an image, identifies the type of noise and attempts to reverse it. The restoration process improves the image by using a priori knowledge of the degradation process. The degradation process first identifies the type of noise, and then apply the inverse process to recover the corrupted image. In this paper various spatial domain filters are discussed which are used to remove noise from the images.

Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain

Image denoising is always the basic problem of image processing, and the main challenge is how to effectively remove the noise and preserve the detailed information. This paper presents a new image denoising algorithm based on the combination of trivariate prior model in nonsubsampled dual-tree complex contourlet transformlet transform (NSDTCT) domain and non-local means filter (NLMF) in spatial domain. Firstly, NSDTCT is constructed by combining the dual-tree complex wavelet transform (DTCWT) and nonsubsampled directional filter banks (NSDFB). The noisy image is decomposed by using NSDTCT. Secondly, based on the correlation between the interscale and intrascale dependencies of NSDTCT coefficients, the distribution of the high frequency coefficients is modeled with the trivariate non-Gaussian distribution model. A nonlinear trivariate shrinkage function is derived in the framework of Bayesian theory, and then the denoised coefficients are obtained and inverse NSDTCT is performed to get the initial denoised image. Finally, NLMF is used to smooth the initial denoised image. Simulation experiment shows that our algorithm can obtain better performances than those outstanding denoising algorithms in terms of peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) as well as visual quality.

Spatial and Frequency Domain Filters for Restoration of Noisy Images

Image restoration is the operation of taking a degraded image and estimating the clean image. Degradation in an image occurs primarily due to blur and noise. Restoration attempts to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Thus restoration techniques are oriented toward modeling the degradation and applying the inverse process in order to recover the original image. This paper starts with basic model of image degradation/restoration process and proceeds towards enlisting commonly occurring blurs and noises. Thereafter, it reviews some spatial and frequency domain filters for restoration of noisy images.

Mixed noise removal filter for multi‐channel images based on halfspace deepest location

In this study, the authors propose a novel method for removing mixed (mixture of impulse and Gaussian) multi-channel noise from multi-channel digital images based on a modified version of the algorithm introduced by Struyf and Rousseeuw (Comput. Stat. Data Anal. (2000), 34, pp. 415–426) for finding approximate halfspace deepest location (Tukey's median). Denoising results of this new nonlinear spatial domain filtering method applied to benchmark images corrupted by multi-channel mixed noise outperform currently used spatial domain filters and state-of-the-art wavelet transform domain filters in terms of both peak signal-to-noise ratio and visual quality. Unlike most of the existing algorithms which remove the noise from multi-channel digital images on each of the channels separately, our method, because of its multivariate/multi-dimensional nature, eliminates the noise on all channels simultaneously without their separation, thus preserving the spectral correlation between channels in a multi-channel image. Proposed denoising method is very effective for removal of very wide range of powers of mixed multi-channel noise, but can be also successfully implemented for reduction of other forms of multi-channel noise since it is independent of the source or distribution of the noise.

How to SAIF-ly Boost Denoising Performance

Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive regression kernel) have achieved great success in denoising. Their overall performance, however, has not generally surpassed the leading transform domain-based filters (such as BM3-D). One important reason is that spatial domain filters lack efficiency to adaptively fine tune their denoising strength; something that is relatively easy to do in transform domain method with shrinkage operators. In the pixel domain, the smoothing strength is usually controlled globally by, for example, tuning a regularization parameter. In this paper, we propose spatially adaptive iterative filtering (SAIF) is the Middle Eastern/Arabic name for sword. This acronym somehow seems appropriate for what the algorithm does by precisely tuning the value of the iteration number. a new strategy to control the denoising strength locally for any spatial domain method. This approach is capable of filtering local image content iteratively using the given base filter, and the type of iteration and the iteration number are automatically optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the estimated local signal-to-noise-ratio, we also present a new risk estimator that is different from the often-employed SURE method, and exceeds its performance in many cases. Experiments illustrate that our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing) parameters, and effectively boost the performance of several existing denoising filters to generate state-of-the-art results under both simulated and practical conditions.

Development of an adaptive bilateral filter for evaluating color image difference

Spatial filtering, which aims to mimic the contrast sensitiv- ity function (CSF) of the human visual system (HVS), has previously been combined with color difference formulae for measuring color image reproduction errors. These spatial filters attenuate impercep- tible information in images, unfortunately including high frequency edges, which are believed to be crucial in the process of scene analysis by the HVS. The adaptive bilateral filter represents a novel approach, which avoids the undesirable loss of edge information introduced by CSF-based filtering. The bilateral filter employs two Gaussian smoothing filters in different domains, i.e., spatial domain and intensity domain. We propose a method to decide the param- eters, which are designed to be adaptive to the corresponding view- ing conditions, and the quantity and homogeneity of information contained in an image. Experiments and discussions are given to support the proposal. A series of perceptual experiments were con- ducted to evaluate the performance of our approach. The experimen- tal sample images were reproduced with variations in six image attributes: lightness, chroma, hue, compression, noise, and sharp- ness/blurriness. The Pearson's correlation values between the model-predicted image difference and the observed difference were employed to evaluate the performance, and compare it with that of spatial CIELAB and image appearance model. © 2012 SPIE and IS&T. (DOI: 10.1117/1.JEI.21.2.023021)

The Application of the Residue Number System in Digital Image Processing: Propose a Scheme of Filtering in Spatial Domain

The Application of the Residue Number System in Digital Image Processing: Propose a Scheme of Filtering in Spatial Domain

Novel method for removal of multichannel impulse noise based on half-space deepest location

A novel method is proposed for removing impulse (random-valued and salt-and-pepper) noise from multichannel digital images based on an adapted version of the DEEPLOC algorithm introduced by A. Struyf and P. J. Rousseeuw for calculation of approximate half-space (Tukey's) deepest location (median) in multivariate case. Due to its intrinsic multivariate/multidimensional nature, the proposed method eliminates the noise simultaneously on all channels without their separation, which preserves the spectral correlation between channels in a multichannel image. Denoising results of this new nonlinear spatial domain filter applied to benchmark images outperform currently used state-of-the-art filters for impulse noise removal from multichannel images in terms of both objective effectiveness criteria [peak-signal-to-noise-ratio (PSNR), mean absolute error (MAE), and normalized color distance (NCD)] and visual quality. The proposed filter successfully preserves the edges and fine image details, and is very effective for removal of medium and heavy multichannel impulse noise.

Comparative analysis on some spatial-domain filters for fringe pattern denoising

Fringe patterns produced by various optical interferometric techniques encode information such as shape, deformation, and refractive index. Noise affects further processing of the fringe patterns. Denoising is often needed before fringe pattern demodulation. Filtering along the fringe orientation is an effective option. Such filters include coherence enhancing diffusion, spin filtering with curve windows, second-order oriented partial-differential equations, and the regularized quadratic cost function for oriented fringe pattern filtering. These filters are analyzed to establish the relationships among them. Theoretical analysis shows that the four filters are largely equivalent to each other. Quantitative results are given on simulated fringe patterns to validate the theoretical analysis and to compare the performance of these filters.

Detecting the Buildings from Airborne Laser Scanner Data by Using Fourier Transform

The automatic extraction of objects from airborne laser scanner data and images has been a topic of research for decades. Laser scanner data have proven to be a powerful source for a wide range of 2D–3D geographic information system object tasks. This paper presents the Fourier transform as an image enhancement tool for determination of buildings from the images generated by laser signal. Spatial and frequency domain filtering techniques have been utilized for extraction of building from enhanced images. While Gaussian and Wiener filterings were selected in frequency domain, Sobel and Unsharp were selected for the spatial domain. The boundaries of buildings have been delineated from the generated images, which were obtained from inverse Fourier transform, by using edge detectors, such as Canny, Sobel, and Prewitt. Frequency domain filters using Fourier transformation were compared with spatial domain filters in the way of kernel function and windows. The reason for doing the filtering in the frequency domain is that it is computationally faster to perform two-dimensional Fourier transforms and a filter is more applicable than to perform a convolution in the spatial domain. Results showed that using Fourier transformation has a great advantage in enhancing images and detecting the buildings on images. Filtering in the frequency domain is more efficient computationally than spatial domain filtering when the filter size is big. The conclusion proved that Fourier transformation can be used as an image enhancement tool to detect and extract buildings automatically.

Suitable post processing algorithms for X-ray imaging using oversampled displaced multiple images

X-ray imaging systems such as photon counting pixel detectors have a limited spatial resolution of the pixels, based on the complexity and processing technology of the readout electronics. For X-ray imaging situations where the features of interest are smaller than the imaging system pixel size, and the pixel size cannot be made smaller in the hardware, alternative means of resolution enhancement require to be considered. Oversampling with the usage of multiple displaced images, where the pixels of all images are mapped to a final resolution enhanced image, has proven a viable method of reaching a sub-pixel resolution exceeding the original resolution. The effectiveness of the oversampling method declines with the number of images taken, the sub-pixel resolution increases, but relative to a real reduction of imaging pixel sizes yielding a full resolution image, the perceived resolution from the sub-pixel oversampled image is lower. This is because the oversampling method introduces blurring noise into the mapped final images, and the blurring relative to full resolution images increases with the oversampling factor. One way of increasing the performance of the oversampling method is by sharpening the images in post processing. This paper focus on characterizing the performance increase of the oversampling method after the use of some suitable post processing filters, for digital X-ray images specifically. The results show that spatial domain filters and frequency domain filters of the same type yield indistinguishable results, which is to be expected. The results also show that the effectiveness of applying sharpening filters to oversampled multiple images increase with the number of images used (oversampling factor), leaving 60–80% of the original blurring noise after filtering a 6 × 6 mapped image (36 images taken), where the percentage is depending on the type of filter. This means that the effectiveness of the oversampling itself increase by using sharpening filters, and more images taken can be considered worth the effort.

The Role of Combined OSR and SDF Method for Pre-Processing of Microarray Data that Accounts for Effective Denoising and Quantification

Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.

Three-dimensional measurement method with orthogonal composite grating aided by fringe contrast and background calibration

A three dimensional (3-D) measurement method with orthogonal composite grating aided by fringe contrast and background calibration is proposed. Due to spectrum overlap, we find that the contrast and background of each deformed phase-shifting fringe demodulated from the captured composite fringe image is changed differently, which violates the principle that the contrast and background between any two deformed fringes must be uniform in the traditional 3-D measurement method with orthogonal composite grating, and therefore results in measurement error. We acquire the phase-shifting sinusoidal fringes from the composite fringe image captured on the reference plane, and the zero- and first-order spectra of the sinusoidal fringes can be obtained by filtering in the spatial frequency domain. Then the ratio contrast and background coefficients between the phase-shifting sinusoidal fringes can be calculated. When an object is measured, the contrast and background of the demodulated deformed fringes can be calibrated by these ratio coefficients. A new 3-D measurement mathematical model is set up to reconstruct the object. Experiments prove the new method can effectively restrain the effect of spectrum overlap, and can improve the measurement accuracy more than 1 time.

Small Target Detection Using Bilateral Filter Based on Edge Component

Bilateral filter (BF) performs sharpness enhancement and noise removal by using two Gaussian filters, the domain filter in spatial domain and the range filter in intensity domain. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF for small target detection with the adaptive standard deviation based on the analysis of the edge component, also having the variable filter size. At first, threshold of pixel edge components for four directions decides whether any pixel is potential small targets or not. For the proposed BF operation for the potential small target pixels, its edge component is mapped to two standard deviations of the domain filter and the range filter in the proposed BF by mapping function. When the BF comes to a target region, the two standard deviations increase in proportion to the edge component to blur the small targets. To further blur the small targets, the filter size of the BF also increases by its edge component. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is more robust and efficient than the conventional methods.

Wavefront reconstruction and three-dimensional shape measurement by two-step dc-term-suppressed phase-shifted intensities

A wavefront reconstruction and three-dimensional (3-D) shape measurement method by a two-step phase-shifting algorithm with arbitrary phase shift in (0,pi) is proposed. In this method, only two phase-shifted intensities, with the removal of the dc term by an averaging technique in spatial domain or low-pass filter operation in the frequency domain, are needed, and the other additional measurements are no longer required. The simulation for an irregular wavefront has shown the feasibility, and the optical experiment for a 3-D face mask in the case of a sinusoidal fringe projection system has illustrated the validity of the proposed method.

수중 인공구조물에 대한 사이드스캔소나 탐사자료의 영상처리

Side scan sonar using acoustic wave plays a very important role in the underwater, sea floor, and shallow marine geologic survey. In this study, we have acquired side scan sonar data for the underwater man-made structures, artificial reefs and fishing grounds, installed and distributed in the survey area. We applied digital image processing techniques to side scan sonar data in order to improve and enhance an image quality. We carried out digital image processing with various kinds of filtering in spatial domain and frequency domain. We tested filtering parameters such as kernel size, differential operator, and statistical value. We could easily estimate the conditions, distribution and environment of artificial structures through the interpretation of side scan sonar.

Circular spatial filtering under high-noise-variance conditions

This paper introduces a novel circular spatial filtering scheme for suppressing additive white Gaussian noise (AWGN) under high-noise-variance conditions. In this method, a circular spatial-domain window, whose weights are derived from two independent functions: (i) spatial distance and (ii) gray level distance, is employed for filtering. The proposed filter is different from the Bilateral filter [Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the IEEE internal conference on computer vision 1998. p. 836-46] and performs well under high-noise conditions. It is capable of smoothing Gaussian noise as well as retaining detailed information of images. It gives significant performance in terms of peak-signal-to-noise ratio (PSNR) and universal quality index (UQI) and outperforms many known existing spatial-domain and wavelet-domain filters. The filtered image also gives better visual quality than the existing methods.

Despeckle and geographical feature extraction in SAR images by wavelet transform

This paper presents a method to despeckle Synthetic Aperture Radar (SAR) image, and then extract geographical features in it. In this research work, speckle is reduced by multiscale analysis in wavelet domain. In terms of geographical feature preservation the result shows that the method is better compared to spatial domain filters, such as Lee, Kuan, Frost, Ehfrost, Median, Gamma filters. The geographical features such as roads, airport runways, rivers and other ribbon-like shape structures are detected by the new wavelet-based method as proposed by Yuan Yan Tang. Experimental results show that the proposed method extracts geographical features of different width as well as different gray levels.

Different sensing techniques for geomorphometric measurements

Solid-state MEMS accelerometers are small and cost-effective new sensors developed within the last decade that have often been used as tilt sensors in non-agricultural environments. In some applications accelerometers can replace electronic clinometers, which are heavily dampened sensors used in common tilt measurements. Our research tested the hypothesis that either sensor can be used as dynamic tilt sensors and accurately map areas with varying topography. Slope is a vector measurement of the rate of change of elevation, and is a soil attribute commonly studied in the geosciences due to the influence it exerts over other soil attributes and crop yield. Mathematical models were used to relate pitch and roll angles of a roving vehicle to field slope gradient and aspect, based on data collected with a dual-axis accelerometer and a dual-axis clinometer in a field test. Benchmark for comparisons were slope values derived from RTK-GPS measurements. Time-domain and spatial-domain filters were applied to the data collected in the field. The coefficient of determination ( r 2), mean absolute error (MAE), and model efficiency (ME) were used to compare slope gradient results. Slope aspect was evaluated on whether it was correctly classified according to cardinal directions. Results suggest that the accelerometer and the clinometer are accurate enough to be used as dynamic tilt sensors. The MAEs of slope gradient were less than 1° when compared to measurements derived from RTK-GPS, for scales similar to Order I soil maps. The correct classification of slope aspect was higher than 70%. Solid-state MEMS accelerometers represent a new and cost-effective technology that can be successfully used in such measurements. Accelerometers outperform clinometers in cost, speed, and simplicity of use, but not in accuracy.