Добірка наукової літератури з теми "Non-blind image restoration"

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.

Blurring image caused by a number of factors such as de focus, motion, and limited sensor resolution. Most of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image. We proposed adaptive blind- non reference image quality assessment method for estimation the blur function (i.e. point spread function PSF) from the image acquired under low-lighting conditions and defocus images using Bayesian Blind Deconvolution. It is based on predicting a sharp version of a blurry inter image and uses the two images to solve a PSF. The estimation down by trial and error experimentation, until an acceptable restored image quality is obtained. Assessments the qualities of images have done through the applications of a set of quality metrics. Our method is fast and produces accurate results.

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

The remote sensing imaging environment is complex, in which many factors cause image blur. Thus, without prior knowledge, the restoration model established to obtain clear images can only rely on the observed blurry images. We still build the prior with extreme pixels but no longer traverse all pixels, such as the extreme channels. The features are extracted in units of patches, which are segmented from an image and partially overlap with each other. In this paper, we design a new prior, i.e., overlapped patches’ non-linear (OPNL) prior, derived from the ratio of extreme pixels affected by blurring in patches. The analysis of more than 5000 remote sensing images confirms that OPNL prior prefers clear images rather than blurry images in the restoration process. The complexity of the optimization problem is increased due to the introduction of OPNL prior, which makes it impossible to solve it directly. A related solving algorithm is established based on the projected alternating minimization (PAM) algorithm combined with the half-quadratic splitting method, the fast iterative shrinkage-thresholding algorithm (FISTA), fast Fourier transform (FFT), etc. Numerous experiments prove that this algorithm has excellent stability and effectiveness and has obtained competitive processing results in restoring remote sensing images.

Images of non-crystalline biological specimens embedded in vitreous ice or carbohydrates have to be recorded at rather high defocus to obtain sufficient contrast. Therefore resolution is normally rather limited. Though it is well-known that image restoration from focus series can provide more complete information, this or related techniques have only rarely been used. However, the recent facilities of external TEM control and electronic image recording suggest the restoration technique should be revived and adapted to the needs of biological structure investigation. As autofocus methods have been shown to work rather accurately and focus steps can be calibrated in advance, it can be expected that the restoration can, in principle, be carried out on line and quasi blind. An additional advantage of image restoration from focus series is that it can be used, particularly in case of rather thick ice layers, to separate the phase part of the image function from most of the background due to multiple scattering by combining under- and overfocus images.

Image deconvolution is an important pre-processing step in image analysis which may be combined with denoising, also an important image restoration technique, and prepares the image to facilitate diagnosis in the case of medical images and further processing such as segmentation and registration. Considering the variational approach to this problem, regularisation is a vital component for reconstructing meaningful information and the problem of defining appropriate regularisation is an active research area. An important question in image deconvolution is how to obtain a restored image which has sharp edges where required but also allows smooth regions. Many of the existing regularisation methods allow for one or the other but struggle to obtain good results with both. Consequently, there has been much work in the area of variational image reconstruction in finding regularisation techniques which can provide good quality restoration for images which have both smooth regions and sharp edges. In this paper, we propose a new regularisation technique for image reconstruction in the blind and non-blind deconvolution problems where the precise cause of blur may or may not be known. We present experimental results which demonstrate that this method of regularisation is beneficial for restoring images and blur functions which contain both jumps in intensity and smooth regions.

L’introduction de dégradations lors du processus de formation d’images est un phénomène inévitable: les images souffrent de flou et de la présence de bruit. Avec les progrès technologiques et les outils numériques, ces dégradations peuvent être compensées jusqu’à un certain point. Cependant, la qualité des images acquises est insuffisante pour de nombreuses applications. Cette thèse contribue au domaine de la restauration d’images. La thèse est divisée en cinq chapitres, chacun incluant une discussion détaillée sur différents aspects de la restauration d’images. La thèse commence par une présentation générale des systèmes d’imagerie et pointe les dégradations qui peuvent survenir ainsi que leurs origines. Dans certains cas, le flou peut être considéré stationnaire dans tout le champ de vue et est alors simplement modélisé par un produit de convolution. Néanmoins, dans de nombreux cas de figure, le flou est spatialement variable et sa modélisation est plus difficile, un compromis devant être réalisé entre la précision de modélisation et la complexité calculatoire. La première partie de la thèse présente une discussion détaillée sur la modélisation des flous spatialement variables et différentes approximations efficaces permettant de les simuler. Elle décrit ensuite un modèle de formation de l’image générique. Puis, la thèse montre que la restauration d’images peut s’interpréter comme un problème d’inférence bayésienne et ainsi être reformulé en un problème d’optimisation en grande dimension. La deuxième partie de la thèse considère alors la résolution de problèmes d’optimisation génériques, en grande dimension, tels que rencontrés dans de nombreux domaines applicatifs. Une nouvelle classe de méthodes d’optimisation est proposée pour la résolution des problèmes inverses en imagerie. Les algorithmes proposés sont aussi rapides que l’état de l’art (d’après plusieurs comparaisons expérimentales) tout en supprimant la difficulté du réglage de paramètres propres à l’algorithme d’optimisation, ce qui est particulièrement utile pour les utilisateurs. La troisième partie de la thèse traite du problème de la déconvolution aveugle (estimation conjointe d’un flou invariant et d’une image plus nette) et suggère différentes façons de contraindre ce problème d’estimation. Une méthode de déconvolution aveugle adaptée à la restauration d’images astronomiques est développée. Elle se base sur une décomposition de l’image en sources ponctuelles et sources étendues et alterne des étapes de restauration de l’image et d’estimation du flou. Les résultats obtenus en simulation suggèrent que la méthode peut être un bon point de départ pour le développement de traitements dédiés à l’astronomie. La dernière partie de la thèse étend les modèles de flous spatialement variables pour leur mise en oeuvre pratique. Une méthode d’estimation du flou est proposée dans une étape d’étalonnage. Elle est appliquée à un système expérimental, démontrant qu’il est possible d’imposer des contraintes de régularité et d’invariance lors de l’estimation du flou. L’inversion du flou estimé permet ensuite d’améliorer significativement la qualité des images. Les deux étapes d’estimation du flou et de restauration forment les deux briques indispensables pour mettre en oeuvre, à l’avenir, une méthode de restauration aveugle (c’est à dire, sans étalonnage préalable). La thèse se termine par une conclusion ouvrant des perspectives qui pourront être abordées lors de travaux futurs
Degradations of images during the acquisition process is inevitable; images suffer from blur and noise. With advances in technologies and computational tools, the degradations in the images can be avoided or corrected up to a significant level, however, the quality of acquired images is still not adequate for many applications. This calls for the development of more sophisticated digital image restoration tools. This thesis is a contribution to image restoration. The thesis is divided into five chapters, each including a detailed discussion on different aspects of image restoration. It starts with a generic overview of imaging systems, and points out the possible degradations occurring in images with their fundamental causes. In some cases the blur can be considered stationary throughout the field-of-view, and then it can be simply modeled as convolution. However, in many practical cases, the blur varies throughout the field-of-view, and thus modeling the blur is not simple considering the accuracy and the computational effort. The first part of this thesis presents a detailed discussion on modeling of shift-variant blur and its fast approximations, and then it describes a generic image formation model. Subsequently, the thesis shows how an image restoration problem, can be seen as a Bayesian inference problem, and then how it turns into a large-scale numerical optimization problem. Thus, the second part of the thesis considers a generic optimization problem that is applicable to many domains, and then proposes a class of new optimization algorithms for solving inverse problems in imaging. The proposed algorithms are as fast as the state-of-the-art algorithms (verified by several numerical experiments), but without any hassle of parameter tuning, which is a great relief for users. The third part of the thesis presents an in depth discussion on the shift-invariant blind image deblurring problem suggesting different ways to reduce the ill-posedness of the problem, and then proposes a blind image deblurring method using an image decomposition for restoration of astronomical images. The proposed method is based on an alternating estimation approach. The restoration results on synthetic astronomical scenes are promising, suggesting that the proposed method is a good candidate for astronomical applications after certain modifications and improvements. The last part of the thesis extends the ideas of the shift-variant blur model presented in the first part. This part gives a detailed description of a flexible approximation of shift-variant blur with its implementational aspects and computational cost. This part presents a shift-variant image deblurring method with some illustrations on synthetically blurred images, and then it shows how the characteristics of shift-variant blur due to optical aberrations can be exploited for PSF estimation methods. This part describes a PSF calibration method for a simple experimental camera suffering from optical aberration, and then shows results on shift-variant image deblurring of the images captured by the same experimental camera. The results are promising, and suggest that the two steps can be used to achieve shift-variant blind image deblurring, the long-term goal of this thesis. The thesis ends with the conclusions and suggestions for future works in continuation of the current work

Le traitement des images satellitaires est considéré comme l'un des domaines les plus intéressants dans les domaines de traitement d'images numériques. Les images satellitaires peuvent être dégradées pour plusieurs raisons, notamment les mouvements des satellites, les conditions météorologiques, la dispersion et d'autres facteurs. Plusieurs méthodes d'amélioration et de restauration des images satellitaires ont été étudiées et développées dans la littérature. Les travaux présentés dans cette thèse se concentrent sur la restauration des images satellitaires par des techniques de filtrage statistique non linéaire. Dans un premier temps, nous avons proposé une nouvelle méthode pour restaurer les images satellitaires en combinant les techniques de restauration aveugle et non aveugle. La raison de cette combinaison est d'exploiter les avantages de chaque technique utilisée. Dans un deuxième temps, de nouveaux algorithmes statistiques de restauration d'images basés sur les filtres non linéaires et l'estimation non paramétrique de densité multivariée ont été proposés. L'estimation non paramétrique de la densité à postériori est utilisée dans l'étape de ré-échantillonnage du filtre Bayésien bootstrap pour résoudre le problème de la perte de diversité dans le système de particules. Enfin, nous avons introduit une nouvelle méthode de la combinaison hybride pour la restauration des images basée sur la transformée en ondelettes discrète (TOD) et les algorithmes proposés à l'étape deux, et nos avons prouvé que les performances de la méthode combinée sont meilleures que les performances de l'approche TOD pour la réduction du bruit dans les images satellitaires dégradées
Satellite image processing is considered one of the more interesting areas in the fields of digital image processing. Satellite images are subject to be degraded due to several reasons, satellite movements, weather, scattering, and other factors. Several methods for satellite image enhancement and restoration have been studied and developed in the literature. The work presented in this thesis, is focused on satellite image restoration by nonlinear statistical filtering techniques. At the first step, we proposed a novel method to restore satellite images using a combination between blind and non-blind restoration techniques. The reason for this combination is to exploit the advantages of each technique used. In the second step, novel statistical image restoration algorithms based on nonlinear filters and the nonparametric multivariate density estimation have been proposed. The nonparametric multivariate density estimation of posterior density is used in the resampling step of the Bayesian bootstrap filter to resolve the problem of loss of diversity among the particles. Finally, we have introduced a new hybrid combination method for image restoration based on the discrete wavelet transform (DWT) and the proposed algorithms in step two, and, we have proved that the performance of the combined method is better than the performance of the DWT approach in the reduction of noise in degraded satellite images

In this thesis we investigate the problem of image restoration. The main focus of our research is to come up with novel algorithms and enhance existing techniques in order to deliver efficient and effective methodologies, applicable in real-time image restoration scenarios. Our research starts with a literature review, which identifies the gaps in existing techniques and helps us to come up with a novel classification on image restoration, which integrates and discusses more recent developments in the area of image restoration. With this novel classification, we identified three major areas which need our attention. The first developments relate to non-blind image restoration. The two mostly used techniques, namely deterministic linear algorithms and stochastic nonlinear algorithms are compared and contrasted. Under deterministic linear algorithms, we develop a class of more effective novel quadratic linear regularization models, which outperform the existing linear regularization models. In addition, by looking in a new perspective, we evaluate and compare the performance of deterministic and stochastic restoration algorithms and explore the validity of the performance claims made so far on those algorithms. Further, we critically challenge the ne- cessity of some complex mechanisms in Maximum A Posteriori (MAP) technique under stochastic image deconvolution algorithms. The next developments are focussed in blind image restoration, which is claimed to be more challenging. Constant Modulus Algorithm (CMA) is one of the most popular, computationally simple, tested and best performing blind equalization algorithms in the signal processing domain. In our research, we extend the use of CMA in image restoration and develop a broad class of blind image deconvolution algorithms, in particular algorithms for blurring kernels with a separable property. These algorithms show significantly faster convergence than conventional algorithms. Although CMA method has a proven record in signal processing applications related to data communications systems, no research has been carried out to the investigation of the applicability of CMA for image restoration in practice. In filling this gap and taking into account the differences of signal processing in im- age processing and data communications contexts, we extend our research on the applicability of CMA deconvolution under the assumptions on the ground truth image properties. Through analyzing the main assumptions of ground truth image properties being zero-mean, independent and uniformly distributed, which char- acterize the convergence of CMA deconvolution, we develop a novel technique to overcome the effects of image source correlation based on segmentation and higher order moments of the source. Multichannel image restoration techniques recently gained much attention over the single channel image restoration due to the benefits of diversity and redundancy of the information between the channels. Exploiting these benefits in real time applications is often restricted due to the unavailability of multiple copies of the same image. In order to overcome this limitation, as the last area of our research, we develop a novel multichannel blind restoration model with a single image, which eliminates the constraint of the necessity of multiple copies of the blurred image. We consider this as a major contribution which could be extended to wider areas of research integrated with multiple disciplines such as demosaicing.

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.

Abstract C-Scans are ways to visualize the sample under study by utilizing the reflections from various levels of non-homogenous wave transfer within the sample. Unlike other imaging techniques, C-scan are usually constructed from the pulse-echoed A-Scans by mapping the 1D windowed signal into a point corresponding to a pixel on the C-scan by calculating its energy within the window. Hence, although the A-scans are predominantly Gaussian in nature, the spatial 1D waveforms mapping into spatial energy values, results in completely unpredictable statistical characteristics. Also, the medium characteristics incorporate distortions that are essentially due to a non-minimum phase system response. Hence, the usual Second Order Statistics (SOS) based identification and deconvolution, i.e., correlation and covariance based techniques, may not work very well in this case. In this paper, an approach is presented to use 4th order cumulants to deconvolve the effects of blurring in the C-Scans due to above-mentioned effects. The proposed approach is completely blind to the source or the type of distortion and the formulation is purely two-dimensional. When the blurring function is modeled as an Auto Regressive (AR) process, the image is restored recursively with the application of the inverse filter based on the AR estimate. A significant improvement in the image quality has been demonstrated. Especially, the edges are detected more prominently than present in the original image. Very little or no post processing is needed to obtain the final image.