Lucy-Richardson Method Research Articles

A Deconvolutional Reconstruction Method Based on Lucy–Richardson Algorithm for Joint Scanning Laser Thermography

Joint scanning laser thermography (JLST) is well-known for its efficiency to overcome the field of view (FOV) limitation of thermal imagers. However, JSLT requires a data reconstruction to reveal the location of the defective area straightforwardly. Moreover, its detection capacity is limited by the lack of a deconvolution algorithm adaptive to the reconstructed data. In this study, a deconvolutional reconstruction method based on the Lucy–Richardson (LR) algorithm has been developed for JST, which is effective in suppressing random noise and the blur effect caused by the thermal diffusion. A JSLT inspection is carried out on a functional coating material with cylinder-like defects to test the performance of the proposed method. In comparison to the directly processed method on the original data, the proposed method is processed on the reconstructed data and then compared with principal component analysis (PCA), restored pseudo heat flux (RPHF), fast Fourier transform (FFT) methods and non-negative matrix factorization (NMF). The experimental results indicated that our proposed LR method exhibited a higher signal-to-noise ratio. Besides, it can detect the cylinder-mocked debonding defects with a diameter of 1.5 mm and a depth of 2.0 mm buried under the 1.0-mm coating. In addition, the defect detection diameter-to-depth ratio reached 1.5, while the defect detection rate of the test specimens can approach 90%.

Optimization of Lucy-Richardson Algorithm Using Modified Tikhonov Regularization for Image Deblurring

In this paper, we propose the method to optimize the iteration number of the Lucy-Richardson algorithm for image deblurring. This technique based on the modified Tikhonov regularization which composed of 2 parts which are designed for measuring the image similarity and noise enhancement due to the deblurring process. The regularization parameter will be used to control the desired deblurred image. Several sizes of the Gaussian blur kernel are applied for generating the degraded image in the simulation experiment. The Peak Signal to Noise Ratio (PSNR) metric is used to measure the deblurring performance. The results show that this method can be used to estimate the optimal iteration number and it also gives the PSNR value higher than the default Lucy-Richardson method and regularized filter all sizes of the experimental blur kernel. Moreover, it also tolerance to the deviated blur kernel especially it smaller than exact blur kernel.

Image Deblurring and Localization for Disaster Response and Humanitarian Assistance

Abstract This paper focuses on enhancing videos taken from a moving camera capable of capturing videos at any speed and in multiple directions. The second objective is to locate humans in the video to be able to conduct surveillance in case of disasters, without risking human lives. Most reported work for disaster management deals with satellite images. Indoor disaster management using image processing has been less explored. This work addresses applications involving disasters like a building on fire, wherein it will be useful to send a camera in to scan the rooms and locate people if present. This allows rescue teams/robots to make informed decisions and act accordingly. In this paper, the working of a 3600 camera is emulated using multiple ordinary cameras and the location of people is estimated using image processing techniques without Internet connectivity. The challenge is to train the model with a small dataset and accurately determine the location without any hardware setup (like sensors). Combination of Blind Deconvolution followed by Lucy-Richardson method gave optimal results. The frames were enhanced by de-noising using median filter, sharpening using unsharp mask and contrast enhancement using histogram equalization in order to make the object detection more accurate. Humans in the video were identified using either body or facial detection. Their locations were estimated with respect to a reference point and were quantified by predicting distance, angle and direction. In order to increase the accuracy, a fine-tuning mechanism has been proposed for location estimation using a non-linear curve, which gave acceptable accuracy within a range of 0.01m.

RETRACTED ARTICLE: Analysis of regional atrophy and prolong adaptive exclusive atlas to detect the alzheimers neuro disorder using medical images

Alzheimer’s Neurodegenerative Disease is understood neurological issue, which is influenced cerebrum cells are causes the intellectual decrease and memory misfortune, the sickness begins gentle and deteriorates. There is a serious necessity to identify Alzheimer’s disease at an earlier stage, therefore that the appropriate treatment can initiate early. The foremost causes for Alzheimer’s diseases are low blood flow and brain activity. So, the serious Alzheimer disease has been recognized by several existing methodologies but they are fails to recognize the disease in the earlier stage. Thus, in this work use Single Photon Emission Nuclear Tomographic Imaging(SPECT) for find the Alzheimer’s diseases. The collected SPECT image noise is removed by applying Lucy-Richardson method and affected region is segmented by using Prolong adaptive exclusive analytical Atlas (PAEA). After this segmentation process Regional Atrophy Analysis (RAA) is apply for find the values of Gray Matters (GM) and White Matters (WM) regions in SPECT image. Finally Artificial Neural Network is applying for classification process. The experimental results shows that the promising outcomes in term of accuracy, sensitivity, and specificity.

Dynamic deformation image de-blurring and image processing for digital imaging correlation measurement

This paper proposes a method for de-blurring of images captured in the dynamic deformation of materials. De-blurring is achieved based on the dynamic-based approach, which is used to estimate the Point Spread Function (PSF) during the camera exposure window. The deconvolution process involving iterative matrix calculations of pixels, is then performed on the GPU to decrease the time cost. Compared to the Gauss method and the Lucy–Richardson method, it has the best result of the image restoration. The proposed method has been evaluated by using the Hopkinson bar loading system. In comparison to the blurry image, the proposed method has successfully restored the image. It is also demonstrated from image processing applications that the de-blurring method can improve the accuracy and the stability of the digital imaging correlation measurement.

$$\nu $$ ν acceleration of statistical iterative methods for image restoration

Image restoration problem is an important topic which appears in many different scientific areas. Several solving techniques are available, but generally in real applications, from which large-scale linear systems arise, the choice falls on iterative algorithms. In particular statistical methods (Lucy–Richardson method, image space reconstruction algorithm) have been extensively studied in the literature. Since they have low convergence rates, it is necessary to employ acceleration strategies. At this time, the most popular is the one introduced in 1997 by Biggs and Andrews, called automatic acceleration by the authors. In the present paper we describe \(\nu \) acceleration, that is the translation of the idea behind \(\nu \)-method, conceived for speeding up Landweber method, in the context of statistical methods. Computational results, which compare accelerated and classical methods, show the effectiveness of this strategy, which is able to get better performance than automatic acceleration.

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

An Image Restoration Method Based on Electromagnetism-like Mechanism Algorithm

A new image restoration algorithm is proposed by combining the Electromagnetism-like Mechanism algorithm with image restoration, because reasons of degradation are very complex and methods used commonly are limited by prior knowledge. First, the degraded image is restored with Lucy-Richardson method, and random noise is added to difierent areas of the restored image to generate the initial population (i.e. the speculated image). Second, the speculated image is used to generate another degraded image by the same degradation. Then, the rational function of these two degraded images is taken to be the objective function to get the optimal restored image by seeking for the maximum value of the objective function. Experimental results show that the image restoration algorithm proposed is good at image restoration and not limited by prior knowledge.

An Automatic Segmentation for Determination of IV Vessel Boundaries

Coronary heart disease ranks as the top disease which causes the death in the Egypt. Intravascular (IV) ultrasound imaging is used along with X-ray coronary angiography to detect vessel pathologies. This paper introduces three automated approaches for detection of lumen and media-adventitia borders in IV images. In the first method, a 2D median filter is used to initialize the lumen and media-adventitia contours. The second method uses the Lucy-Richardson method and the 2D median filter to determine the lumen border. Method three is a combination of two methods to combine their advantages. The catheter boundaries were automatically determined. This method shows a higher accuracy and ability to work under artifacts and noisy conditions.

High-resolution terahertz reflective imaging and image restoration

We present a high-resolution terahertz (THz) reflective imaging system, operating at 2.52 THz, that employs a continuous-wave THz gas laser and a pyroelectric detector. The spatial resolution was evaluated from the system's modulation transfer function and tested by scanning a series of resolution targets. To further improve the image quality, Lucy-Richardson method was adopted to restore the scanning result. With the scanning spot profile measured using knife edge method, a satisfying restoration result can be obtained. Finally, the system's performance was observed by imaging some different test objects.

An Adaptively Accelerated Lucy-Richardson Method for Image Deblurring

We present an adaptively accelerated Lucy-Richardson (AALR) method for the restoration of an image from its blurred and noisy version. The conventional Lucy-Richardson (LR) method is nonlinear and therefore its convergence is very slow. We present a novel method to accelerate the existing LR method by using an exponent on the correction ratio of LR. This exponent is computed adaptively in each iteration, using first-order derivatives of the deblurred image from previous two iterations. Upon using this exponent, the AALR improves speed at the first stages and ensures stability at later stages of iteration. An expression for the estimation of the acceleration step size in AALR method is derived. The superresolution and noise amplification characteristics of the proposed method are investigated analytically. Our proposed AALR method shows better results in terms of low root mean square error (RMSE) and higher signal-to-noise ratio (SNR), in approximately 43% fewer iterations than those required for LR method. Moreover, AALR method followed by wavelet-domain denoising yields a better result than the recently published state-of-the-art methods.

Restoration of astronomical images by an iterative superresolution algorithm

In this work a new fast method for super-resolving noisy images is described. The iterative technique is similar to the positive constraint algorithm but faster and more eective in terms of the resulting global noise in the image. The astronomical use of the technique is illustrated by applying it to the LMC 49-50 (04/01/98) image. Results from this method are compared with those of well-known algorithms such as the Wiener filter, the positive constraint, the maximum a posteriori probability, and the Lucy-Richardson method.

The Gravitational Lens B1608+656. I.V‐,I‐, andH‐BandHubble Space TelescopeImaging

We present a multiwavelength analysis of high-resolution observations of the quadruple lens B1608+656 from the Hubble Space Telescope archive, acquired with WFPC2 through filters F606W (V band) and F814W (I band) and with NIC1 in filter F160W (H band). In the three bands, the observations show extended emission from the four images of the source in a ringlike configuration that surrounds the two resolved, ensing galaxies. B1608+656 was discovered as a double-lobed radio source and later identified as a poststarburst galaxy in the optical. Based on photometry and optical spectroscopy we estimate that the stellar population of the source has an age of ~500 Myr. This provides a model for the spectrum of the source that extends over spectral regions where no observations are available and is used to generate Tiny Tim point-spread functions (PSFs) for the filters. Deconvolutions performed with the Lucy-Richardson method are presented, and the limitations of these restorations are discussed. V-I and I-H color maps show evidence of extinction by dust associated with one of the lensing galaxies, a late-type galaxy presumably disrupted after its close encounter with the other lens, an elliptical galaxy. The extinction affects the two lens galaxies and two of the four multiple images. The diagnostic of wavelength-dependent effects in the images shows that corrections for contamination with light from the lenses, extinction, and PSF convolution need to be applied before using the extended structure in the images as a constraint on lens models. We will present the restoration of the images in a subsequent paper.

Restoration of interferometric images

The Software Package AIRY (Astronomical Image Restoration in interferometrY) described in this paper is designed for simulation and/or multiple deconvolution of interferometric images. It was conceived for applications to the Large Binocular Telescope (LBT), but can also be used for future interferometers of the same class. AIRY is modular, IDL-based, and designed to be used together with the CAOS (Code for Adaptive Optics Systems) Application Builder. As concerns multiple deconvolution AIRY includes an implementation of the method OS-EM (Ordered Subsets-Expectation Maximization) which is an accelerated version of the Lucy-Richardson method. After a description of the structure and modules of the software package, its use is illustrated with a few applications. In particular the intrinsic performance of the implemented restoration method is explored in the case of binary stars of different angular separations and magnitude differences, as well as in the case of a diffuse object. The possibility of obtaining super-resolved images of unresolved binary stars is also demonstrated.