Phase Stretch Transform Research Articles

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques

Anisotropic Phase Stretch Transform-Based Algorithm for Segmentation of Activated Sludge Phase-Contrast Microscopic Image

The activated sludge (AS) process is a biological treatment process of wastewater used in sewage treatment plants, in which settling of AS is vitally important for wastewater treatment. In AS, however, bulking caused by filamentous bacteria will significantly reduce the settling capacity of activated sludge. Traditionally, the physicochemical method has been used to monitor the status or performance of filamentous bacteria in activated sludge, while it is a very compromising means in modern digital quality control for wastewater treatment when digital image processing and analysis technology is used to determine the status of filamentous bacteria in activated sludge. In order to avoid the disadvantages of deficient direction selectivity in the translation vector field for isotropic phase stretch transform (PST) and high noise susceptibility in determining the status of filamentous bacteria in AS and low accuracy, an anisotropic PST (APST) method for filamentous bacteria test in AS was proposed in this paper; specifically speaking, by analyzing and deriving the disadvantages of the traditional isotropic PST kernel function, we designed an APST kernel function, put forward a non-maximum suppression strategy for processing of images from different aspects and combined with the relative total variation theory to form APST-based algorithm for segmentation of activated sludge phase-contrast microscopic image. According to a large number of experimental results, in terms of the overall segmentation effect or the subjective and objective evaluation indicators, the resultant algorithm in this paper is superior to the latest traditional PST segmentation algorithm, Canny image segmentation algorithm and Sobel image segmentation algorithm. Also, the segmentation results for filamentous bacteria showed that probability rand index (PRI) and global consistency error (GCE) indicators were all improved by about 30%, supporting the effectiveness and superiority of the algorithm in this paper.

Classification of Drones Using Edge-Enhanced Micro-Doppler Image Based on CNN

The development of advanced radar system for detection and classification of UAVs is an essential requirement for today’s societal security. Such intelligent system could able to analyze the received radar signal and extract relevant information by utilizing sophisticated algorithm. In this letter, the utilization of micro-Doppler signature (MDS) for classification of drones, using convolutional neural network (CNN) model has been presented. We have generated images of micro-Doppler signatures using W-band radar system and used it for classification purpose. In this work, phase stretch transform (PST) has been utilized for edge detection and enhancement of the micro-Doppler images, to generate the edge-enhanced micro-Doppler image (EMDI). The comparison based on classification performance of CNN with different input datasets shows that the EMDI based CNN model outperformed the micro-Doppler image (MDI) based model.

Super-Resolution Based on Residual Learning and Optimized Phase Stretch Transform

High resolution infrared (IR) images are often required in military and industrial applications. Due to the limited properties of IR imaging sensors and camera lens, IR images exhibit poor spatial resolution with a blur phenomenon in the edge regions. In this correspondence, we develop a new super-resolution (SR)-IR image reconstruction method using the residual learning network in the wavelet domain (WRESNET) and optimized phase stretch transform (PST). Our algorithm first transforms the input low resolution (LR)-IR image into its low-frequency and high-frequency subbands using the discrete wavelet decomposition. Subsequently, we introduce the optimized PST to operate on the LR-IR image and extract the intrinsic edge structure. The PST behaves differently at low-frequency and high-frequency regions, thus capturing the intensity variations for edge detection. We incorporate the PST extracted edge map in the wavelet subbands to preserve the intrinsic structure of images. The resultant subbands are further refined based on the missing residuals obtained using the WRESNET. The proposed method is validated through quantitative and qualitative evaluations against the conventional and state-of-art SR methods. Results reveal that the proposed method outperforms the existing methods.

An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation.

The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure and improper detail processing. This paper attempts to improve the treatment effect of Phase Stretch Transform (PST) on the information of low and medium frequencies. For this purpose, an image enhancement algorithm on the basis of fractional-order PST and relative total variation (FOPSTRTV) is developed to address the task. In this algorithm, the noise in the original image is removed by low-pass filtering, the edges of images are extracted by fractional-order PST, and then the images are fused with extracted edges through RTV. Finally, extensive experiments were used to verify the effect of the proposed algorithm with different datasets.

基于扩展相位拉伸变换的多聚焦图像融合算法

基于扩展相位拉伸变换的多聚焦图像融合算法

Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

Acquiring clear images is a requisite in visual sensor networks. Image enhancement is an effective way to improve image quality. In this paper, non-sub-sampled shearlet transform (NSST) multi-scale analysis is combined with phase stretch transform (PST) to nonlinearly enhance the images captured by visual sensors. The components of different scales after NSST multi-scale decomposition are processed by nonlinear models with different thresholds. The thresholds of the enhanced model are determined by the local standard deviation of PST feature map. The noise is well suppressed, and the detail features are enhanced obviously. Experiments show that the proposed algorithm can improve image distortion, clear details, and enhance image contrast effectively.

Leak detection optimisation on retina fluorescein angiography images using phase stretch transform for malaria retinopathy

Malarial Retinopathy (MR) is indicated by retina alteration such as white dots occurrence which is caused by malaria. Leak detection is a key factor of MR’s early diagnosis. Inconsistent size and shape of the leakages with the colour contrast that relatively similar with the background. Leak detection’s algorithm is one of the most complex algorithms on the fundus image analysis field. Therefore, improving performance in the leakage detection is essential. This study focuses on automated leakage detection on fluorescein angiography (FA) images. The methods used in this study are vessel segmentation, saliency detection, phase stretch transform (PST), optic disk removal and leak detection to extract some features which then classified to correctly validate the leak. From 20 patient data large focal leak images with 31 leak points, 28 of them have been correctly detected. So, the experiment produced the accuracy and specificity of 0.98 and 0.9, respectively. With the proposed method of this study, there is a potential to enhance the knowledge on MR field in the future.

Multi-Focus Image Fusion Algorithm in Sensor Networks

Most imaging systems have a limited depth-of-field in the sensor networks that consist of multiple visual sensors. Due to different object distances, not every object can be clearly imaged by a single sensor. This paper proposes a multi-focus image fusion algorithm in sensor networks. The algorithm combines the advantages of multi-scale analysis and image phase analysis. It uses dynamic window and phase stretch transform (PST) to extract the focused regions of each image accurately and performs multi-resolution analysis of images through the non-sub-sampled shearlet transform. Then, the images are fused according to the local standard deviation of PST feature maps. The results of the simulation experiments prove that our algorithm is effective and outperforms some state-of-the-art algorithms.

Feature Enhancement in Visually Impaired Images.

One of the major open problems in computer vision is feature detection in visually impaired images. In this paper, we describe a potential solution using Phase Stretch Transform, a new computational approach for image analysis, edge detection and resolution enhancement that is inspired by the physics of the photonic time stretch technique. We mathematically derive the intrinsic nonlinear transfer function and demonstrate how it leads to (1) superior performance at low contrast levels and (2) a reconfigurable operator for hyper-dimensional classification. We prove that the Phase Stretch Transform equalizes the input image brightness across a range of intensities resulting in high dynamic range in visually impaired images. We also show further improvement in the dynamic range by combining our method with the conventional techniques. Finally, our results propose a new paradigm for the computation of mathematical derivatives via group delay dispersion operations.