32 research outputs found
Enhancement of Single and Composite Images Based on Contourlet Transform Approach
Image enhancement is an imperative step in almost every image processing algorithms.
Numerous image enhancement algorithms have been developed for gray scale images
despite their absence in many applications lately. This thesis proposes hew image
enhancement techniques of 8-bit single and composite digital color images. Recently, it
has become evident that wavelet transforms are not necessarily best suited for images.
Therefore, the enhancement approaches are based on a new 'true' two-dimensional
transform called contourlet transform. The proposed enhancement techniques discussed
in this thesis are developed based on the understanding of the working mechanisms of the
new multiresolution property of contourlet transform. This research also investigates the
effects of using different color space representations for color image enhancement
applications. Based on this investigation an optimal color space is selected for both single
image and composite image enhancement approaches. The objective evaluation steps
show that the new method of enhancement not only superior to the commonly used
transformation method (e.g. wavelet transform) but also to various spatial models (e.g.
histogram equalizations). The results found are encouraging and the enhancement
algorithms have proved to be more robust and reliable
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
Analysis and Denoising of Hyperspectral Remote Sensing Image in the Curvelet Domain
A new denoising algorithm is proposed according to the characteristics of hyperspectral remote sensing image (HRSI) in the curvelet domain. Firstly, each band of HRSI is transformed into the curvelet domain, and the sets of subband images are obtained from different wavelength of HRSI. And then the detail subband images in the same scale and same direction from different wavelengths of HRSI are stacked to obtain new 3-D datacubes of the curvelet domain. Again, the characteristics analysis of these 3-D datacubes is performed. The analysis result shows that each new 3-D datacube has the strong spectral correlation. At last, due to the strong spectral correlation of new 3-D datacubes, the multiple linear regression is introduced to deal with these new 3-D datacubes in the curvelet domain. The simulated and the real data experiments are performed. The simulated data experimental results show that the proposed algorithm is superior to the compared algorithms in the references in terms of SNR. Furthermore, MSE and MSSIM in each band are utilized to show that the proposed algorithm is superior. The real data experimental results show that the proposed algorithm effectively removes the common spotty noise and the strip noise and simultaneously maintains more fine features during the denoising process
Enhancement of Single and Composite Images Based on Contourlet Transform Approach
Image enhancement is an imperative step in almost every image processing algorithms.
Numerous image enhancement algorithms have been developed for gray scale images
despite their absence in many applications lately. This thesis proposes hew image
enhancement techniques of 8-bit single and composite digital color images. Recently, it
has become evident that wavelet transforms are not necessarily best suited for images.
Therefore, the enhancement approaches are based on a new 'true' two-dimensional
transform called contourlet transform. The proposed enhancement techniques discussed
in this thesis are developed based on the understanding of the working mechanisms of the
new multiresolution property of contourlet transform. This research also investigates the
effects of using different color space representations for color image enhancement
applications. Based on this investigation an optimal color space is selected for both single
image and composite image enhancement approaches. The objective evaluation steps
show that the new method of enhancement not only superior to the commonly used
transformation method (e.g. wavelet transform) but also to various spatial models (e.g.
histogram equalizations). The results found are encouraging and the enhancement
algorithms have proved to be more robust and reliable
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy
Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
An increasing need for biometrics recognition systems has grown substantially to
address the issues of recognition and identification, especially in highly dense areas
such as airports, train stations, and financial transactions. Evidence of these can be
seen in some airports and also the implementation of these technologies in our mobile
phones. Among the most popular biometric technologies include facial, fingerprints,
and iris recognition. The iris recognition is considered by many researchers to be the
most accurate and reliable form of biometric recognition because iris can neither be
surgically operated with a chance of losing slight nor change due to aging. However,
presently most iris recognition systems available can only recognize iris image with
frontal-looking and high-quality images. Angular image and partially capture image
cannot be authenticated with the existing method of iris recognition. This research
investigates the possibility of developing a technique for recognition partially captured
iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%,
and 12.5% and to find a threshold for a minimum amount of iris region required to
authenticate the individual. The research also developed and implemented two
Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre
wavelet filter is to enhance the feature extraction technique. Selected iris images from
CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced
technique. The technique was able to produce recognition accuracy between 70 – 90%
CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with
74.95%, and MMU with 94.45%
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising
Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods
Feature-preserving image restoration and its application in biological fluorescence microscopy
This thesis presents a new investigation of image restoration and its application to
fluorescence cell microscopy. The first part of the work is to develop advanced image
denoising algorithms to restore images from noisy observations by using a novel featurepreserving
diffusion approach. I have applied these algorithms to different types of
images, including biometric, biological and natural images, and demonstrated their
superior performance for noise removal and feature preservation, compared to several
state of the art methods. In the second part of my work, I explore a novel, simple and
inexpensive super-resolution restoration method for quantitative microscopy in cell
biology. In this method, a super-resolution image is restored, through an inverse process,
by using multiple diffraction-limited (low) resolution observations, which are acquired
from conventional microscopes whilst translating the sample parallel to the image plane,
so referred to as translation microscopy (TRAM). A key to this new development is the
integration of a robust feature detector, developed in the first part, to the inverse process
to restore high resolution images well above the diffraction limit in the presence of strong
noise. TRAM is a post-image acquisition computational method and can be implemented
with any microscope. Experiments show a nearly 7-fold increase in lateral spatial
resolution in noisy biological environments, delivering multi-colour image resolution of
~30 nm