Adaptive Equalization Technique Research Articles

A 1.62–5.4-Gb/s Receiver for DisplayPort Version 1.2a With Adaptive Equalization and Referenceless Frequency Acquisition Techniques

We present a 1.62–5.4-Gb/s receiver for DisplayPort version 1.2a and propose an adaptive equalizer (EQ) with a peak-level comparison technique for eye measurement. A single comparator and an up/down unmatched-current charge pump are used to realize a simpler EQ architecture with low power dissipation. A referenceless frequency acquisition technique is also proposed. A time-to-digital converter-based pulsewidth detector supports the referenceless frequency acquisition within the range of 1.62–5.4 Gb/s. An XOR-gate-embedded charge pump and a half-rate linear phase detector were used to improve the jitter tolerance (JTOL) performance. The measured eye opening of the proposed EQ at 5.4 Gb/s was 0.68 UI with a −20-dB loss channel. The proposed receiver passed all the JTOL tests of the DisplayPort compliance specification version 1.2b. The power consumption of the receiver was 36.8 mW at 5.4 Gb/s. The receiver occupied a core area of 0.265 mm2 using 65-nm CMOS process technology.

Fuzzy Based Contrast Enhancement Method for Lung Cancer CT Images

Computer aided diagnosis (CAD) has become most commonly used tool in the diagnosis and analysis of medical images such as Lung cancer detection, Brest cancer detection etc. Different type of medical image modalities are used in the diagnosis process by radiologist and physician such as radiographic images ,computer tomography (CT) images, magnetic resonance images (MRI), positron emission tomography (PET) etc. In all analysis if the image quality is not proper then the performance of the CAD system is adversely effected. This work proposed a new Background suppressed fuzzy contrast enhancement (BSFCE) method to improve contrast of the CT images of lung cancer patient. The performance of proposed method have been extensively evaluated and compared with the two existing contrast enhancement techniques Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE) techniques in terms of Mean, Variance and Contrast-per-pixel (CPP).The results reflects that the proposed method outperforms the HE and AHE techniques.

Impact of Different Training Mode on Adaptive Equalization Techniques for MIMO-OFDM System

Impact of Different Training Mode on Adaptive Equalization Techniques for MIMO-OFDM System

Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization

Cephalometric images usually have low contrast. The existing techniques for automatic cephalometric analysis usually use histogram equalization for image enhancement. This technique has the advantage of being fully automatic and nonlinear. However, it suffers from spikes, excessive enhancement, and lack of brightness preservation. The proposed technique is an adaptive histogram equalization technique that uses wavelet based gradient histograms. This paper compares its performance with two traditional techniques, three histogram modification based techniques, and two wavelet based techniques. Forty digital and scanned cephalograms are used to conduct tests. In addition to visual histograms and intensity profiles, the proposed method is compared in terms of eight quantitative measures. The various measures are applied to analyze the results in terms of contrast enhancement (EME, CNR), brightness preservation (AMBE), edge conservation and enhancement (H, TEN), preservation of image structures and non-addition distortion (MSSIM, SVD-M). The proposed method gives good contrast enhancement, with better brightness preservation without losing edge information and with the minimum addition of distortions to the enhanced cephalometric images.

Pre-processing of Mammography Image for Early Detection of Breast Cancer

Breast cancer is one of the most prevalent causes of death among women worldwide. Hence, the early detection helps to save the life of the women. Mammography is the basic screening test for breast cancer. It consist many artefacts, which negatively influences in detection of the breast cancer. Therefore, removing artefacts and enhancing the image quality is a required process in Computer Aided Diagnosis (CAD) system. The accuracy and efficiency of the CAD is increased by providing exact Region of Interest (ROI). Extracting ROI is a challenging task in preprocessing because the presence of pectoral muscle influences the detection of abnormality. Here, the proposed show that the wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) techniques efficiently aids for enhancing the quality of the image, thereby it also removes the unwanted background and the pectoral muscle by using thresholding and modified region growing technique respectively. Furthermore, the proposed algorithm was tested on mini-MIAS database; the result obtained was compared with completeness and correctness for pectoral muscle removal and was reported as 98% and 97% respectively. Collectively, these results suggest that the proposed method is well suited for improving the quality of mammography image for Auto-CAD system. General Terms Computer Science, Digital Image Processing, Cancer Imaging.

A TECHNIQUE FOR VISUAL QUALITY ENHANCEMENT IN CAPSULE ENDOSCOPY IMAGES USING CONTRAST ENHANCEMENT ALGORITHMS

Capsule Endoscopy is a kind of technology that uses tiny disposable capsule which transmits captured images wirelessly of entire gastro-intestinal tract. The motive of proposed method is to enhance the visual quality in the capsule endoscopy images which is critical mainly for diagnosis. In this paper, primarily median filtering is done to reduce the noise. Next, Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is used to boost the local details visibility by improving the contrast of its local regions. Then it is followed by unsharp filtering, used to sharpen the obtained image. Finally, contrast stretching is used for adjusting the contrast of the image. This yields the enhanced image which highlights more details as well as improves the overall contrast, which is very helpful for diagnosis.

Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

INTENSITY ENHANCEMENT ON OUTDOOR IMAGES

Outdoor images that are captured in bad weather conditions have low contrast and infidelity colours. Under the turbid medium conditions such as haze, mist, fog and drizzle, the light which reaches to the sensor is attenuated by atmospheric particles. These atmospheric phenomena degrade the contrast intensity of outdoor images based on haze density. In this research, we present new method to improve both the intensity and fine details of outdoor scene images. The RGB (Red, Green and Blue) input image is converted to the HSI (Hue Saturation Intensity) colour space and the density of the haze is estimated. Then, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance the degraded intensity based on the estimation of the density of the haze. Our method is effective in a wide range of weather conditions and under different levels of visibility.

Design and Implementation of a Blind Adaptive Equalizer Using Frequency Domain Improved Square Contour Algorithm

Improved Square Contour Algorithm (ISCA) is a blind adaptive equalization technique which employs estimated outputs from the decision device along with the dispersion constant in its cost function, thereby, forcing the equalizer outputs to lie on multiple zero error square moduli instead of a single contour in Square Contour Algorithm (SCA). This leads to greater ISI suppression and an improved convergence rate. In this paper, frequency domain implementation of block based ISCA is developed. Simulation results show that the FDISCA exhibits similar performance as time domain ISCA but with a significantly lower computational complexity. However, by employing normalization in the frequency bins, a noteworthy improvement in the convergence rate for FD-ISCA is observed. Complete hardware architecture of FD-ISCA along with its simulation results is also discussed.

Adaptive filtering algorithms for channel equalization in wireless communication

The major problem in today’ wireless communications is time dispersion and inter symbol interference. To combat these issues various adaptive equalization techniques are used. In literature state of art algorithms of adaptive equalization will be studied in detail, however the utmost goal of these algorithms is to attain high convergence rate, less complexity and least error. In this paper a comparison between least mean square algorithm and fractional least mean square algorithm is presented and experimentally proved that the rate of convergence is high in case of fractional least mean square algorithm. Also it is observed that LMS algorithm has better performance for random signals and fractional LMS has proved very efficiently in case of deterministic signals. These both algorithms are implemented in MATLAB and eight channels with different tap weights are used but in this paper only selected are presented.

A comparative study on preprocessing techniques in diabetic retinopathy retinal images: Illumination correction and contrast enhancement

To investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal image quality, a comparative study was carried out. We studied and implemented a few illumination correction and contrast enhancement techniques on color retinal images to find out the best technique for optimum image enhancement. To compare and choose the best illumination correction technique we analyzed the corrected red and green components of color retinal images statistically and visually. The two contrast enhancement techniques were analyzed using a vessel segmentation algorithm by calculating the sensitivity and specificity. The statistical evaluation of the illumination correction techniques were carried out by calculating the coefficients of variation. The dividing method using the median filter to estimate background illumination showed the lowest Coefficients of variations in the red component. The quotient and homomorphic filtering methods after the dividing method presented good results based on their low Coefficients of variations. The contrast limited adaptive histogram equalization increased the sensitivity of the vessel segmentation algorithm up to 5% in the same amount of accuracy. The contrast limited adaptive histogram equalization technique has a higher sensitivity than the polynomial transformation operator as a contrast enhancement technique for vessel segmentation. Three techniques including the dividing method using the median filter to estimate background, quotient based and homomorphic filtering were found as the effective illumination correction techniques based on a statistical evaluation. Applying the local contrast enhancement technique, such as CLAHE, for fundus images presented good potentials in enhancing the vasculature segmentation.

Multi-scale morphological image enhancement of chest radiographs by a hybrid scheme

Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation.

Model Based Non-Rigid Registration Framework for High Dynamic Range Mammography

Mammography screening is the most prominent method for finding breast cancer at an early stage. The acquired mammograms are High Dynamic Range images having a 12 bit gray scale resolution. When viewed by a radiologist, a single image must be examined several times, each time focusing on a different intensity range. So this paper proposes the Computer aided approach for enhancing standard lesions in digital mammograms, where we have developed a Model based Non-rigid Registration Framework (MBNRF) for High Dynamic Range mammogram image enhancement in a fully automatic way. The proposed system considers two views of mammogram images such as MLO (Mediolateral Oblique View) and CC (Craniocaudal) for processing and consists of two parts: 1) Preliminary processing operations involves in use of Contrast limited Adaptive Histogram Equalization technique to remove noise and intensity in homogeneities and 2) Registering the CC (Craniocaudal) model with MLO (Mediolateral Oblique View) mammogram view image by entropy based registration. The algorithm’s performance has been tested on few mammography images in collaboration with radiologists.

English

The recent digital transmission systems impose the application of channel equalizers with short training time. Equalization techniques compensate for the time dispersion introduced by communication channels. Adaptive equalization is the technique used to reliably transmit data through a communication channel. Ideally, if the channel is ideal, we can demodulate the signal perfectly at the output without causing any error. However, in practice, all the channels are non-ideal and noisy in nature. So, to recover the original signal after demodulation, equalization filter is used, which will minimize the error between original transmitted signal and demodulated signal passed through equalization filter. Given a channel of unknown impulse response, the purpose of an adaptive equalizer is to operate on the channel output such that the cascade connection of the channel and the equalizer provides an approximation to an ideal transmission medium. A replica of transmitted sequence is made available at the receiver in proper synchronism with the transmitter, thereby making it possible for adjustments to be made to the equalizer coefficients in accordance with the LMS algorithm employed in the equalizer design. In this paper, an overview of the current state of the art in adaptive equalization technique has been presented for the different binary encoded sequences like Unipolar, polar and bipolar with and without return to zero encoding.

Steganalysis Technique on Gray Scale Image by Varying Message Length using Adaptive Histogram Equalization Attack

considered as a technique for secret communication without knowing the others and only an authorized user can access the information which is embedding in the cover medium. Steganalysis is the process to detect of presence of Steganography and to destroy, extract it. In this Paper There is Adaptive Histogram Equalization Technique that performed on the Stego Image that is category of the Histogram Equalization attack and measuring the quality of Image after applying the Attack.

A Novel Biometric System Based Hand Vein Recognition

Abstract As a reliable and universal biometric characteristic, hand vein identification has attracted many interested researchers. The hand vein identification system exhibits several excellent advantages in the biometric domain because it meets the increasing demand of accuracy and robustness. In this paper, we propose a new biometric recognition system based on hand vein features. The detection and extraction of the region of interest is based on Voronoi Decomposition. Furthermore, contrast enhancement is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Starting with the basic Gaussian Matched Filter (GMF) and its variant, we propose a new technique called the Improved Gaussian Matched Filter method (IMPGMF) surmounting the false detection of hand vessels with the traditional GMF. Feature points are then detected based on ending and bifurcation structures in the image map obtained with the proposed IMPGMF method and taken as signature for our biometric system. Then, Artificial Neural Networks (ANN) are used for the classification step. In the validation step, we used a 1500 hand vein image from the BOSPHORUS database. The Error Equal Rate is 0.01 % and the Area Under curve of the corresponding system is approximately 0.98, demonstrating a very high security level.

AMELIORATE FUZZY C-MEANS: AN AMELIORATE FUZZY C-MEANS CLUSTERING ALGORITHM FOR CT-LUNG IMAGE SEGMENTATION

Effective and efficient image segmentation acts as a preliminary stage for the computer-aided diagnosis of medical images. For image segmentation, many FCM-based clustering techniques have been proposed. Regrettably, the existing FCM technique does not generate accurate and standardized segmentation results. This is due to the noise present in the image as well as the random initialization of membership values for pixels. To address this issue, this study has enhanced the existing FCM technique and proposed a technique named Ameliorate FCM (AFCM). Initially, the given image is preprocessed to remove the noise using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. The preprocessed image is given as input to a Bayesian classifier to classify the images into two set namely normal and abnormal using a Hybrid feature selection method. The classified images are given as input to the proposed segmentation technique, which overcomes the drawbacks of existing FCM technique. Here, the membership value of the pixels of an image is standardized and clustered to segment the regions. Experiments are carried out using lung images to determine the efficiency of the proposed technique. Results of the experiment show that the proposed technique outperforms the existing FCM technique.

The Application of the Image Edge Detection in Analysis of Composite Insulators Hydrophobicity

Image edge detection technology which is applied to the hydrophobic grade discrimination of composite insulator is of great significance. How to accurately detect the edge of the water droplets is very important in the process of automatic identification of composite insulator hydrophobicity. This paper detects composite insulator hydrophobic image edge with image edge detection technology. Firstly, adaptive histogram equalization technique is used to enhance image, reducing the similarity of target and background which is caused by the transparency of water. Then the paper uses the improved Canny algorithm which is introduced Ostu threshold method to detect the image edge. The experimental results show that the improved algorithm can accurately acquire the water droplets edge of composite insulator surface, which can lay a foundation for automatic discrimination of composite insulator hydrophobic grade.

Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images

This paper establishes an efficient color space for the contrast enhancement of myocardial perfusion images. The effects of histogram equalization and contrast limited adaptive histogram equalization are investigated and the one which gives good enhancement results is extended to the suitable color space. The color space which gives better results is chosen experimentally. Uniqueness of this work is that contrast limited adaptive histogram equalization technique is applied to the chrominance channels of the cardiac nuclear image, leaving the luminance channel unaffected which results in an enhanced image output in color space.

Parametric Optimization and Analysis of Adaptive Equalization Algorithms for Noisy Speech Signals

Adaptive equalization technique plays key role in modern digital communication systems. Adaptive digital filters are widely used in the area of signal processing such as echo cancellation, noise cancellation, channel equalization and beamforming. This paper presents the performance analysis of RLS and CMA algorithm in presence of noisy audio signal. The analysis shows that in CMA algorithm the convergence rate is low as compare to RLS algorithm whereas CMA algorithm requires low computing power and relatively better performance. The parameters of proposed equalizer have been optimized and simulated using Simulink. Index Terms— CMA, Equalization, Noisy audio signal, RLS algorithms