Vijayshri Chaurasia

Random-valued impulse noise removal using adaptive dual threshold median filter

Adaptive dual threshold based noise detection.Averaging based dual threshold computation.Simple median filter based noise removal.Improved de-noised image quality in terms of PSNR. Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.

Removal of high density salt-and-pepper noise by recursive enhanced median filtering

In many applications the existence of impulsive noise in the acquired images is one of the most common problems. In present scenario median filters are very functional solution for the removal of impulse noise in the images. In general procedure firstly the noise is detected and then removed secondly. This paper proposes a novel method with two thresholds (Minimum threshold and maximum threshold) which detects noise more efficiently. Further the enhanced median value is use to replace the noisy pixels recursively. It furnishes a better Peak Signal to Noise Ratio (PSNR) as compared to many existing median filter based image de-noising methods.

Speed Up Technique for Fractal Image Compression

Fractal Image compression is a novel technique in the field of image compression; it is based on affine contractive transforms and utilizes the existence of self-symmetry in the image. This technique has grabbed much attention in recent years because of manifold advantages, very high compression ratio, high decompression speed, high bit-rate and resolution independence. Since 1990 a lot of techniques and improvements have been published in this field. This review represents survey of most significant advances in the field of fractal image compression.

Microstrip planer antenna array by proximity fed for complete S band applications

A naval single band antenna presented is a combination of four element patches, printed on a substrate, for 3.4 GHz frequency operation. The array of antenna uses the ground at the bottom of substrate, a patch at top of substrate fed with proximity coupling. This array has a very simple structure; with size of 50×50mmexcited by discrete port through proxy feeding, which is also having efficient radiator to contribute the resonance for antenna to cover the complete S-Band. It provides the impedance bandwidth of 1.72GHz and Gain of 7.39dB. This antenna is coupled with 50 ohm where modeled through the CST microwave studio antenna software, where it uses finite element method for designing the antenna. The proposed microstrip antenna shows the good impedance bandwidth matching, high gain, high radiation efficiency and high directivity. Results are precisely obtained and validation of model is also verified.

A Review on Magnetic Resonance Images Denoising Techniques

Medical image denoising is a very important and challenging area in the field of image processing. Magnetic resonance imaging is a very popular and most effective imaging technique. During the acquisition, MR images get affected by random noise which could be modeled as Gaussian or Rician distribution. In the past few decades, a wide variety of denoising techniques have been proposed. This paper presents a survey of advancements proposed for the denoising of magnetic resonance images. The performance of most significant image denoising domains has been analyzed qualitatively as well as quantitatively on the basis of mean square error and peak signal-to-noise ratio.

MRI Segmentation for Computer-Aided Diagnosis of Brain Tumor: A Review

Brain tumor is an uncontrolled growth of cells in the brain. Diagnosis of brain tumor is complicated and challenging task as the brain itself a complex structure and tumor have excessive variety, diversity in shape, large range in intensity and ambiguous boundaries. The validity of brain tumor segmentation is a significant issue in biomedical signal processing because it has a direct impact on surgical groundwork. Detection of brain tumor by magnetic resonance imaging (MRI) using (CAD) involves; preprocessing, segmentation, and morphological operation for analysis purpose. The magnetic resonance imaging segmentation is characterized by a high nonuniformity of both the pathology and the surrounding non-pathologic brain tissue. Computer Aided Diagnosis system can assist in the detection of suspicious brain disease as the manual segmentation is time-consuming and it reported the time-varying result. To tag tumor pixel or trace tumor area, texture and pattern remembrance, classification is performed with different algorithms. This article presents an overview of the most relevant brain tumor segmentation methods.

Multifeature-based medical image segmentation

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. This study presents a local feature-based novel technique for the segmentation of histopathology images. It mainly focuses on a system that segments overlapped nuclei (OLN) without affecting the general non-OLN segmentation performance. The proposed method suggests a three-stage system. The initial segmentation is done by using local features for the demarcation of nuclei regions. In the second stage, salient-based active contour is applied for complete nucleus-region identification. In the final step, the OLN are identified and segmented using a Gaussian distribution and entropy maximisation. The performance of the proposed segmentation method is evaluated on the basis of precision, recall, accuracy, and F 1 -score. The proposed method is simulated on animal diagnostics laboratory histopathology image dataset and reported 90.3% average accuracy with average F 1-score 0.937. Simulation results confirm the superiority of the proposed method as compared with the existing state-of-art methods.

Experimental demonstration of all optical XOR logic using SOA based Mach-Zehnder interferometer

We propose and demonstrate all optical XOR logic implemented with SOA based Mach-Zehnder Interferometer, simulated at ultrahigh speed of 40 Gbps. XOR logic is a vital element for signal processing. Non linear characteristics of SOA i.e. cross phase modulation is exploited for XOR logic design. Obtained results confirm the feasibility of XOR logic.

Fractal image compression with optimized domain pool size

Fractal image compression is an inventive method with a large number of advantages. Fractal process utilizes the existence of self-similarity in images. In practical applications, widely used approach of fractal image compressing has very high computational and time requirement of compression phase due to large number of image level comparison between sub-sections of image. It leads to a very high compression time. This paper presents optimization of domain pool size by aimed to provide acceptable retrieved image quality with affordable encoding time.

All Optical SOA-MZI-Based Encryption Decryption System Using Co Propagating Optical Pulses and CW Wave at 40 Gb/s

This paper presents novel optical encryption and decryption systems using a semiconductor optical amplifier at 40 Gb/s. Proposed scheme exploits cross-phase modulation phenomenon in SOA. Our design is mainly based on SOA Mach-Zehnder interferometer structure, optical couplers, CW light, and EDFA. We demonstrate that our implementation is more feasible than conventional SOA-MZI encryption system, where only single optical pulse source is used. Experimental evaluation using eye diagrams shows robustness of our proposed encryption decryption system against eavesdropping.