Homomorphic Filtering Research Articles

Pre‐processing, registration and selection of adaptive optics corrected retinal images

In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Adaptive optics corrected retinal images are pre-processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross-correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross-correlation and maximum-likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and Procrustes transformation. We have found that a wavelet approach (Daubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a 'Designer metric' works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub-pixel translation measured using parabolic interpolation on the peak of the cross-correlation function and maximum-likelihood estimation are found to give very similar results (RMS difference 0.047pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal-to-noise. The sharpness map of the registered and de-rotated images shows increased sharpness over most of the field of view. Adaptive optics assisted images of the cone photoreceptors can be better pre-processed using a wavelet approach. These images can be assessed for image quality using a 'Designer Metric'. Two-stage image registration including correcting for rotation significantly improves the final image contrast and sharpness.

A Segmentation Method for Uneven Illumination Particle Images

To the question of particle images segmentation with uneven illumination background, a novel method is proposed based on homomorphic filtering, Top-Hat transformation and watershed algorithm in this study after discussing the characteristic of various common segmentation algorithms. Firstly, homomorphic filtering is carried out on particle image frequency region space, which weakened low frequency component and strengthened the high frequency component appropriately, to make the whole image evenly. Then, Top-Hat transformation is adopted to remove a large area of the target background and segment the target's active area. Finally, watershed algorithm is used for segmentation of adhesive particles. Experimental results show that the proposed method is simple and practical and can segment the targets form uneven illumination particle images.

Digital Medical Image Enhanced by wavelet Illumination-Reflection Model

When a digital medical image is enhanced, the useful details of the image should be strengthened， but the details can not be strengthened by these algorithms based on traditional illumination-reflection model. According to the imaging principle and medical requirement, wavelet illumination-reflection model and a new algorithm based on the model are proposed. The image is decomposed into illumination and reflection by wavelet illumination-reflection model. The details of the reflection are strengthened. The dynamic range of the illumination is reduced in order to enhance the image. Experiments and analysis show that the method is obviously better than Histogram Equalization, Homomorphic Filtering and multi-scales Retinex. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1866

Signal Dependent Rician Noise Denoising Using Nonlinear Filter

MR images are increasingly used for diagnostic and surgical procedures, as they offer better soft tissue contrast and advanced imaging capabilities. Similar to other imaging modalities, MR images are also subjected to various forms of noises and artifacts. The noise affecting MRI images is known as Rician noise and displays a nonlinear and signal dependent behavior. In this paper we propose a nonlinear filtering method for Rician noise denoising. Nonlinear filters are more capable in addressing signal dependent behavior of noise and offer good denoising with better edge preserving capabilities. A nonlinear filter based on homomorphic filter characteristics has been designed to address Rician noise in MR images. The proposed filter has been implemented on synthetic images and MR images of the articular cartilage. The efficiency of the proposed filtering method is verified by computing the PSNR and SSIM index of the image. The proposed nonlinear filter performs good denoising with improvement in the image quality as observed from the PSNR values of the image. It also offers edge preservation and can be used for both structural MRI and soft tissue study effectively.

Multistage histopathological image segmentation of Iba1-stained murine microglias in a focal ischemia model: Methodological workflow and expert validation

A multistage workflow was developed for segmenting and counting murine microglias from histopathological brightfield images, in a permanent focal cerebral ischemia model. Automated counts are useful, since for the assessment of inflammatory mechanisms in ischemic stroke there is a need to quantify the brain's responses to post-ischemia, which primarily is the rapid activation of microglial cells. Permanent middle cerebral artery occlusion was induced in murine brain tissue samples. Positive cells were quantified by immunohistochemistry for the ionized calcium-binding adaptor molecule-1 (Iba1) as the microglia marker. Microglia cells were segmented in seven sequential steps: (i) contrast boosting using quaternion operations, (ii) intensity outlier normalization, (iii) nonlocal total variation denoising, (iv) histogram specification and contrast stretching, (v) homomorphic filtering, (vi) global thresholding, and (vii) morphological filtering. Workflow counts were validated on an image subset, with ground-truth data acquired from manual counts conducted by a neuropathologist. Automated workflow matched ground-truth counts pretty well; 80–90% accuracy was achieved, as regards to time after pMCAO and correspondence to ischemic/non-ischemic tissue.

Vision-based Classification of Driving Postures by Efficient Feature Extraction and Bayesian Approach

Reports of traffic accidents show that a considerable percentage of the accidents are caused by human factors. Human-centric driver assistance systems, with integrated sensing, processing and networking, aim to find solutions to this problem and other relevant issues. The key technology in such systems is the capability to automatically understand and characterize driver behaviors. In this paper, we propose a novel, efficient feature extraction approach for driving postures from a video camera, which consists of Homomorphic filter, skin-like regions segmentation, canny edge detection, connected regions detection, small connected regions deletion and spatial scale ratio calculation. With features extracted from a driving posture dataset we created at Southeast University (SEU), holdout and cross-validation experiments on driving posture classification are then conducted using Bayes classifier. Compared with a number of commonly used classification methods including naive Bayes classifier, subspace classifier, linear perception classifier and Parzen classifier, the holdout and cross-validation experiments show that the Bayes classifier offers better classification performance than the other four classifiers. Among the four predefined classes, i.e., grasping the steering wheel, operating the shift gear, eating a cake and talking on a cellular phone, the class of talking on a cellular phone is the most difficult to classify. With Bayes classifier, the classification accuracies of talking on a cellular phone are over 90 % in holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction method and the importance of Bayes classifier in automatically understanding and characterizing driver behaviors towards human-centric driver assistance systems.

Digital Medical Image Enhanced by Stationary Wavelet Transform

Abstract: When a digital medical image is enhanced by Histogram Equalization, Homomorphic Filtering or multi-scales Retinex, the useful details of the image can not be strengthened. According to the imaging principle and medical requirement, a new method is proposed. The image is decomposed into illumination and reflection by wavelet illumination-reflection model. The details of the reflection are strengthened by soft-threshold method. The dynamic range of the illumination is reduced by Gaussian filtering in order to enhance the image. Experiments and analysis show that the method is obviously better than these algorithms.

Filtering of Electronic Speckle Correlation Fringes

Speckle correlation fringes include a large quantity of speckle noise. It is first to consider how to reduce speckle noise before using phase formula to calculate. Filter is usually used to reduce speckle noise. From two aspects of frequency domain and space domain, we adopted mean filter, medium filter, Butterworth low-pass filter and homomorphism filter to process the speckle correlation fringes obtained by experiment. It is shown that the speckle correlation fringes are clearer and more easily interpreted and automatic processed after being filtered.

Segmentation of the retinal optic nerve head using Hough transform and active contour models

Optic nerve head is part of the retina where ganglion cell axons exit the eye to form the optic nerve. Glaucomatous changes related to loss of the nerve fibers decrease the neuroretinal rim and expand the area and volume of the cup. This study implements the detection of the optic nerve head in retinal fundus images based on the Hough Transform and Active Contour Models. The process starts with the image enhancement using homomorphic filtering for illumination correction, then proceeds with the removal of blood vessels on the image to facilitate the subsequent segmentation process. T he result of the Hough Transform fitting circle becomes the initial level set for the active contour model. The experimental results show that the implemented segmentation algorithms are capable of segmenting optic nerve head with the average accuracy of 75.56%.

Automatic extraction of foreground objects from Mars images

A novel method is proposed to automatically extract foreground objects from Martian surface images. The characteristics of Mars images are distinct, e.g. uneven illumination, low contrast between foreground and background, much noise in the background, and foreground objects with irregular shapes. In the context of these characteristics, an image is divided into foreground objects and background information. Homomorphism filtering is first applied to rectify brightness. Then, wavelet transformation enhances contrast and denoises the image. Third, edge detection and active contour are combined to extract contours regardless of the shape of the image. Experimental results show that the method can extract foreground objects from Mars images automatically and accurately, and has many potential applications.

同态滤波法抑制离轴数字全息零级项

In consideration of the crosstalk between reconstructed image and zero-order image existed in the digital reconstruction for off-axis digital holography,this paper presents a method to suppress the zero-order image.Based on the principle that the pixel gray-scale of the image is decided both by the image illumination and surface reflectance,the hologram is considered as a product of the incident and reflected components.The incident components which change slowly are mainly concentrated in the low-frequency region,and the reflected components which determine the detail of the image are concentrated in the high-frequency region.A reasonable homomorphic filter with the upper and lower limits of 0.001 and 1 respectively and a diameter of 300 pixel is designed and processed into the hologram.The results show that the zero-order image can be suppressed with only one Fresnel digital hologram at a distance of 34 cm.Meanwhile,the contrast and the details of reconstructed image at the same distance are improved.

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.

Illumination Invariant Face Recognition Using Fuzzy LDA and FFNN

The most significant practical challenge for face recognition is perhaps variability in lighting intensity. In this paper, we developed a face recognition which is insensitive to large variation in illumination. Normalization step including two steps, first we used Histogram truncation as a pre-processing step and then we implemented Homomorphic filter. The main idea is that, achieving illumination invariance causes to simplify feature extraction module and increases recognition rate. Then we utilized Fuzzy Linear Discriminant Analysis (FLDA) in feature extraction stage which showed a good discriminating ability compared to other methods while classification is performed using Feedforward Neural Network (FFNN). The experiments were performed on the ORL (Olivetti Research Laboratory) face image database and the results show the present method outweighs other techniques applied on the same database and reported in literature.

Research on Removing Shadow in Workpiece Image Based on Homomorphic Filtering

The shadow in the image has a great influnece for the accuracy of the image segmentation and measurement, while the workpiece is detected in the machine vision system. In the paper a homomorphic filter algorithm is designed for removing shadow, through the analysis of the relationship between the shadow and the brightness of the image. Firstly the image from RGB form is converted to HSV form, based on the relationship between the variation of the Vcomponent and the shadow, the components of the related shadow can be identified. Then a filter is designed, which uses the principle of homomorphic filtering to filter the component of the related shadow in the image. Finally, a new complete image is formed by using the method of image synthesis to incorporate the treated V-component into the original image. The experiment results show that this method can remove the cast shadows in the image effectively, which has improved the image quality for the subsequence image processing.

Comparative study of retrospective methods to reduce non-uniform illumination effects to bridge coating

Abstract Digital image processing methods have been actively studied and applied to various infrastructure components including bridge painting assessment. Despite the advantages of image recognition techniques to bridge coating, the results can be greatly affected by the non-uniform illumination of target conditions. It is necessary to first convert all images to a standardized background illumination condition. The effect caused from non-uniform illumination can be minimized by setting lighting conditions properly or capturing additional calibration images. However, capturing additional calibration images is not possible in many practical situations and hence a good method of shading correction is required. This paper addresses the way to correct digital bridge coating images containing non-uniform illumination, which becomes more difficult when it comes to assessing facility surfaces with rust defects. In order to choose the most appropriate one, four representative shading correction methods were selected and tested to identify their suitability to processing bridge coating images. These methods include: (1) image smoothing, (2) homomorphic filtering, (3) morphological filtering, and (4) entropy minimization method. Test results on bridge coating images showed that the entropy minimization method outperformed the other three methods.

Comparative Study on Filter Methods of Medical Ultrasound Images

This paper presents a comparative study on six despeckling methods such as modified hybrid median filter, gabor filter, speckle reducing anisotropic diffusion, homomorphic filter, non-local mean filter and squeeze box filter. We select eight objective evaluation parameters, such as signal-to-ratio, contrast signal–to–noise ratio, figure of merit, least absolute error, peak signal-to-noise ratio, edge protection factor, quantitative parameters of despeckling, signal-to-minimum mean square error ratio, to quantify the performance of these filters. The comparative study will provide a good guidance for selecting a suitable filter in the ultrasound image processing.

Comparative Analysis of various Illumination Normalization Techniques for Face Recognition

The change in facial appearance due to illumination variation degrades face recognition systems performance considerably. In this paper, various states of art illumination normalization techniques have been explained and compared. The classification of the image recognition has been done using artificial neural networks (ANN). We have compared four illumination normalization methods which are (1) discrete cosine transform (DCT) with rescaling of low frequency coefficients (2) discrete cosine transform (DCT) with discarding of low frequency coefficients (3) homomorphic filtering (HF) (4) gamma intensity correction (GIC). These methods are evaluated and compared on Yale and Yale B Faces databases.

Exposing Image Fuzzy Forgeries based on Dyadic Contrast Contourlet

In this paper, we combined with efficient image signal processing algorithms, according to the manifestations of the blur of polish operation in the frequency domain,proposed a blind detection algorithm for image tampering evidence. First, the target image transformed into a new Nonsubsampled dyadic contourlet domain which based on contrast a trous wavelet, then homomorphic filtering on it in this domain to enhance the edge whcih was blurred, and then combined with morphological Filter to eroded the area of non-tampering, to complete the blurred image tampering detection. Through experiments, the algorithm in this paper have a excellent recognition for the blurred retouch images.

Improved algorithm for removing thin cloud in single remote sensing image

Because the algorithm of cloud threshold often generates boundary effect,this paper proposed an improved algorithm based on wavelet transform and homomorphic filter.The image with cloud was decomposed by wavelet transform to find the proper number of demarcation levels.Cloud could be removed by making homomorphic filtering to the higher level coefficients,while giving the lower level detailed coefficients and the approximation coefficients some weight factors respectively.The three parts of coefficients were reconstructed and fused to get processed result.The experimental results indicate that the proposed algorithm can remove the thin cloud cover effectively,maintain the details better and prevent producing the boundaries.