Restoration and enhancement of astronomical images using hybrid adaptive nonlinear complex diffusion-based filter

In this paper, a hybrid and adaptive nonlinear complex diffusion based technique is proposed for restoration and enhancement of astronomical images corrupted with additive noise due to diffractions limit, aberrations in telescope camera lens, atmospheric irregularities which are modelled by Gaussian functions. In addition to additive noise removal, the proposed filter is also capable of removing noisy stars and spike noises due to other numerous stars that may have surrounded an object in the astronomical image such as in nebula images. The proposed scheme is completely adaptive in nature in the sense that it estimates all the required filtering parameters from the observed image itself. The performance of the proposed hybrid scheme is compared with other image restoration techniques such as averaging filter, Gaussian filter, median filter, anisotropic diffusion based filter, local variance based adaptive anisotropic diffusion filter and also the adaptive and hybrid version of anisotropic diffusion filter similar to the proposed one in terms of average SNR and BSNR. The results obtained show the efficacy of the proposed scheme.Defence Science Journal, 2012, 62(6), pp.437-442, DOI:http://dx.doi.org/10.14429/dsj.62.129

Mean of Median Absolute Derivation Technique for Speckle Noise Variance Estimation in Computerised Tomography Images

The accurate estimation of noise variance in an image is the first important stage in image filtering using adaptive filters. In this paper, a new technique for the estimation of speckle noise present in Computerised Tomography (CT) lung image was developed. The development of mean of median absolute derivation technique based on the estimated mean of speckle noise present in CT images is presented. From the result of the simulations, the new technique gave a reasonably accurate estimate of variance of speckle noise present in CT Images. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% where used as test images. Also, the new technique gave the lowest average speckle noise variance estimation error of 2.53% compared to 12.53% for the Median of Median Absolute Derivative Technique, 18.18% for the Transfer function technique and 37.14% for the Mode of Variance Technique. The simulation software used in the paper is Matrix Laboratory (MATLAB2012).http://dx.doi.org/10.4314/njt.v34i2.2

1 Indirect estimation of signal-dependent noise with non-adaptive heterogeneous samples

Abstract—We consider the estimation of signal-dependent noise from a single image. Unlike conventional algorithms that build a scatterplot of local mean-variance pairs from either small or adaptively selected homogeneous data samples, our proposed approach relies on arbitrarily large patches of heterogeneous data extracted at random from the image. We demonstrate the feasibility of our approach through an extensive theoretical analysis based on mixture of Gaussian distributions. A prototype algorithm is also developed in order to validate the approach on simulated data as well as on real camera raw images. Index Terms—Noise estimation, signal-dependent noise, Poisson noise

Local estimation of the noise level in MRI using structural adaptation

We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1-data corrupted with artificial noise. Simulation results are compared with a recently proposed estimate. The method was applied to a high-resolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data

Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance

Edge Aware Anisotropic Diffusion for 3D Scalar Data

Fig. 1: The left half of the figure demonstrates the consistency in smoothing of our method compared to the existing method. The right half of the figure demonstrates the de-noising capabilities of our method. All the images from (a-c) were obtained byrenderingan iso-surface of 153. (a) Diffused with an existing diffusion model proposed by Krissian et al. [20] with k = 40, and100 iterations (b) The original Sheep’s heart data. (c) Diffused with our method with σ = 1 and the same number of iterations. The yellow circle indicates aregionwheretheiso-surfacehasbothhighandmediumrangegradient magnitude, and the blue circle marks a region where the gradient magnitude is much lower. Note the inconsistent smoothing in (a) inside the yellow circle. (d) The tooth data contaminated with Poisson noise (SNR=12.8) (e)Theoriginaltoothdata(f)Diffusedwithourmethod(SNR=25.76) withσ = 1 and 25 iterations. We used the exact same transfer function to render all the images in(d-f). Abstract—Inthispaperwepresentanovelanisotropicdiffusionmodel targeted for 3D scalar field data. Our model preserves material boundaries as well as fine tubular structures while noise is smoothed out. One of the major novelties is the use of the directional second derivative to define material boundaries instead of the gradient magnitude for thresholding. This results in a diffusion model that has much lower sensitivity to the diffusion parameter and smoothes material boundaries consistently compared to gradient magnitude based techniques. We empirically analyze the stability and convergence of the proposed diffusion and demonstrate its de-noising capabilities for both analytic and real data. We also discuss applications in the context of volume rendering

Improved Non-Local Means Algorithm Based on Dimensionality Reduction

Non-Local Means is an image denoising algorithm based on patch similarity. It compares a reference patch with the neighboring patches to find similar patches. Such similar patches participate in the weighted averaging process. Most of the computational time for Non-Local Means is consumed to measure patch similarity. In this thesis, we have proposed an improvement where the image patches are projected into a global feature space. Then we have performed a statistical t-test to reduce the dimensionality of this feature space. Denoising is achieved based on this reduced feature space and the proposed modification exploits an improvement in terms of denoising performance and computational time

Models and Methods for Estimation and Filtering of Signal-Dependent Noise in Imaging

The work presented in this thesis focuses on Image Processing, that is the branch of Signal Processing that centers its interest on images, sequences of images, and videos. It has various applications: imaging for traditional cameras, medical imaging, e.g., X-ray and magnetic resonance imaging (MRI), infrared imaging (thermography), e.g., for security purposes, astronomical imaging for space exploration, three-dimensional (video+depth) signal processing, and many more.This thesis covers a small but relevant slice that is transversal to this vast pool of applications: noise estimation and denoising. To appreciate the relevance of this thesis it is essential to understand why noise is such an important part of Image Processing. Every acquisition device, and every measurement is subject to interferences that causes random fluctuations in the acquired signals. If not taken into consideration with a suitable mathematical approach, these fluctuations might invalidate any use of the acquired signal. Consider, for example, an MRI used to detect a possible condition; if not suitably processed and filtered, the image could lead to a wrong diagnosis. Therefore, before any acquired image is sent to an end-user (machine or human), it undergoes several processing steps. Noise estimation and denoising are usually parts of these fundamental steps.Some sources of noise can be removed by suitably modeling the acquisition process of the camera, and developing hardware based on that model. Other sources of noise are instead inevitable: high/low light conditions of the acquired scene, hardware imperfections, temperature of the device, etc. To remove noise from an image, the noise characteristics have to be first estimated. The branch of image processing that fulfills this role is called noise estimation. Then, it is possible to remove the noise artifacts from the acquired image. This process is referred to as denoising.For practical reasons, it is convenient to model noise as random variables. In this way, we assume that the noise fluctuations take values whose probabilities follow specific distributions characterized only by few parameters. These are the parameters that we estimate. We focus our attention on noise modeled by Gaussian distributions, Poisson distributions, or a combination of these. These distributions are adopted for modeling noise affecting images from digital cameras, microscopes, telescopes, radiography systems, thermal cameras, depth-sensing cameras, etc. The parameters that define a Gaussian distribution are its mean and its variance, while a Poisson distribution depends only on its mean, since its variance is equal to the mean (signal-dependent variance). Consequently, the parameters of a Poisson-Gaussian distribution describe the relation between the intensity of the noise-free signal and the variance of the noise affecting it. Degradation models of this kind are referred to as signal-dependent noise.Estimation of signal-dependent noise is commonly performed by processing, individually, groups of pixels with equal intensity in order to sample the aforementioned relation between signal mean and noise variance. Such sampling is often subject to outliers; we propose a robust estimation model where the noise parameters are estimated optimizing a likelihood function that models the local variance estimates from each group of pixels as mixtures of Gaussian and Cauchy distributions. The proposed model is general and applicable to a variety of signal-dependent noise models, including also possible clipping of the data. We also show that, under certain hypotheses, the relation between signal mean and noise variance can also be effectively sampled from groups of pixels of possibly different intensities.Then, we propose a spatially adaptive transform to improve the denoising performance of a specific class of filters, namely nonlocal transformdomain collaborative filters. In particular, the proposed transform exploits the spatial coordinates of nonlocal similar features from an image to better decorrelate the data, and consequently to improve the filtering. Unlike non-adaptive transforms, the proposed spatially adaptive transform is capable of representing spatially smooth coarse-scale variations in the similar features of the image. Further, based on the same paradigm, we propose a method that adaptively enhances the local image features depending on their orientation with respect to the relative coordinates of other similar features at other locations in the image.An established approach for removing Poisson noise utilizes so-called variance-stabilizing transformations (VST) to make the noise variance independent of the mean of the signal, hence enabling denoising by a standard denoiser for additive Gaussian noise. Within this framework, we propose an iterative method where at each iteration the previous estimate is summed back to the noisy image in order to improve the stabilizing performance of the transformation, and consequently to improve the denoising results. The proposed iterative procedure allows to circumvent the typical drawbacks that VSTs experience at very low intensities, and thus allows us to apply the standard denoiser effectively even at extremely low counts.The developed methods achieve state-of-the-art results in their respective field of application

Anàlisi de patrons funcional i estructurals en la regulació del calci en les cèl·lules cardíaques

Tesi sotmesa a embargament des de la data de defensa fins al dia 16 de setembre de 2022In 2019 the data published by the World Health Organization showed that the cardiac ischemia is the first cause of death worldwide. These diseases are caused by irregularities of the contractile mechanism’s function, starting from the cellular level till the organ dysfunction. To further understand the origin of these dysfunctions the thesis will focus on the molecular and cellular level. The small changes of the physiology at that level can lead to an anomalous cardiac electrophysiology. The previous studies suggest that there is a relation between the cardiac arrhythmia and the increase of global spontaneous calcium activity in cells. Ryanodine receptors have the major role in the calcium regulation. They release the calcium stored in the sarcoplasmic reticulum into the cytosol, then the calcium attaches to the actin and myosin to produce the mechanical contraction of the cell. The irregularities have been also related with an hyperphosphorylation of the ryanodine receptor channels, which increases the open probability of those. A large amount of data is generated in the biology and physiology laboratories, these have to be characterized, clustered and analysed to extract the meaning of the observed divergences. Thus, an interdisciplinary thesis has been developed in order to obtain experimental data with a confocal microscope in the laboratory and then creating new image processing tools in order to have an interpretable analysis of the experimental data. The main objective of the project is to characterize the spontaneous calcium activity through image processing tools and to observe its variations under different physiological conditions, such as the hiperphosphorylation of the ryanodine receptor channels or under cardiac arrhythmia conditions. The aim is to see how the modifications in the calcium activity affect to the propagation along the cell and report how the variations may lead to electric and contractile dysfunctions, and so, cause arrhythmias. To reach this goals the thesis is divided into three main sections or hypothesis: 1. Experimental data acquisition, define the best image acquisition method in order to develop the studies of calcium images and proposal of methods to detect subcellular structures and spontaneous calcium events. 2. Study of the calcium events substructure and the involved ryanodine receptor clusters. Comparison of the calcium activity properties of the cells at the basal level and under conditions of hyperphosphorylation or arrhythmia. 3. Study of the physiological modifications in the calcium activity through different phosphorylation pathways.The results explain the ryanodine receptor clusters distribution through the cells, allow to have the localization of the channels and the calcium activity simultaneously and show relevant details such as, the increase in the duration of the events or the increase in the volume of calcium released when more ryanodine receptor clusters are co-activated. These modifications lead to have bigger events and, thus, more potentially dangerous because can depolarize the neighbouring cells. The parameters reflect the relationship between the phosphorylation of the ryanodine receptors and the spontaneous calcium release in the arrhythmogenesis process.Segons les dades publicades per l'Organització Mundial de la Salut el 2019, la isquèmia cardíaca és la causa de mortalitat que ocupa el primer lloc a nivell mundial. Aquesta patologia és deguda a irregularitats en el funcionament del mecanisme contràctil, començant a nivell cel·lular, fins arribar a nivell d'òrgan. Per arribar a l'arrel d'aquestes disfuncions, en aquesta tesi es realitzarà un estudi a nivell cel·lular i molecular. Els petits canvis en aquest nivell poden arribar a escalar cap a una electrofisiologia cardíaca anòmala. Durant els darrers anys, els estudis de fisiologia cardíaca han relacionat les arítmies amb l'augment de l'activitat espontània de calci global a nivell cel·lular. Els principals encarregats de regular el calci en les cèl·lules dels miòcits cardíacs són els receptors de rianodina, que alliberen el calci emmagatzemat al reticle sarcoplasmàtic al citosol. Aquest s'uneix a l'actina i la miosina de la cèl·lula produint la contracció mecànica. Les irregularitats en l'activitat de calci han estat relacionades amb la fosforilació d'aquests canals, ja que un cop fosforil·lats la probabilitat d'obertura dels receptors augmenta. Als laboratoris de biologia i fisiologia es treballa amb volums relativament grans dades experimentals, que posteriorment s'han de caracteritzar, agrupar i donar un sentit als canvis observats. En vista d'aquest fet, s'ha volgut realitzar una tesi interdisciplinària, adquirint dades amb el microscopi confocal al laboratori i creant noves eines de processament d'imatges per facilitar una anàlisi interpretable de les dades experimentals. L'objectiu principal del projecte és caracteritzar l'activitat espontània del calci mitjançant tècniques de processament d’imatge i observar les variacions d'aquesta amb diferents condicions fisiològiques, com ara la fosforilació dels receptors de rianodina o amb cèl·lules de pacients amb arítmia. Veure com les modificacions en l'activitat de calci afecten a la propagació d'aquest al llarg de la cèl·lula i caracteritzar la perillositat de les variacions a l’hora d'induir un mal funcionament elèctric i contràctil i, per tant, donar a lloc arítmies. En motiu d'assolir aquest objectius la tesis es divideix en tres grans apartats o hipòtesis: 1. Obtenció de dades experimentals, definició del millor mètode per realitzar els estudis de les imatges de calci i proposta d’un mètode de detecció i quantificació d'estructures cel·lulars i esdeveniments de calci. 2. Estudi de la subestructura dels esdeveniments de calci i els clústers de receptors de rianodina involucrats. Comparació de les propietats de l'activitat de calci de les cèl·lules a nivell basal i sota condicions d'arítmia o fosforilació. 3. Estudi dels canvis electrofisiològics en l’activitat del calci emprant diferents vies de fosforilació dels canals de receptors de rianodina. Els resultats obtinguts expliquen la distribució dels receptors de rianodina en les cèl·lules, permeten tenir una visió de les activacions d'aquests canals durant l'activitat espontània de calci i mostren detalls rellevants, com ara l'augment de la durada, l'augment en el volum de calci alliberat i l'augment del nombre de clústers receptors de rianodina activats. Aquestes modificacions provoquen esdeveniments més grans i, per tant, més potencialment perillosos, ja que poden arribar a despolaritzar cèl·lules veïnes. Aquests paràmetres reflecteixen la relació entre la fosforilació dels receptors de rianodina i l'alliberament espontani de calci durant el procés d'aritmogènesi.Postprint (published version