Denoising and enhancement of mammographic images under the assumption of heteroscedastic additive noise by an optimal subband thresholding

Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation

Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild

Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting. The unique novelty of our approach is its ability to generate low-level representations of image quality that are complementary to high-level features representing image content. We refer to the framework used to train the two encoders as Re-IQA. For Image Quality Assessment in the Wild, we deploy the complementary low and high-level image representations obtained from the Re-IQA framework to train a linear regression model, which is used to map the image representations to the ground truth quality scores, refer Figure 1. Our method achieves state-of-the-art performance on multiple large-scale image quality assessment databases containing both real and synthetic distortions, demonstrating how deep neural networks can be trained in an unsupervised setting to produce perceptually relevant representations. We conclude from our experiments that the low and high-level features obtained are indeed complementary and positively impact the performance of the linear regressor. A public release of all the codes associated with this work will be made available on GitHub.Comment: Accepted to IEEE/CVF CVPR 2023. Code will be released post conference in July 2023. Avinab Saha & Sandeep Mishra contributed equally to this wor

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound

Enhanced perception in volume visualization

Due to the nature of scientic data sets, the generation of convenient visualizations may be a difficult task, but crucial to correctly convey the relevant information of the data. When working with complex volume models, such as the anatomical ones, it is important to provide accurate representations, since a misinterpretation can lead to serious mistakes while diagnosing a disease or planning surgery. In these cases, enhancing the perception of the features of interest usually helps to properly understand the data. Throughout years, researchers have focused on different methods to improve the visualization of volume data sets. For instance, the definition of good transfer functions is a key issue in Volume Visualization, since transfer functions determine how materials are classified. Other approaches are based on simulating realistic illumination models to enhance the spatial perception, or using illustrative effects to provide the level of abstraction needed to correctly interpret the data. This thesis contributes with new approaches to enhance the visual and spatial perception in Volume Visualization. Thanks to the new computing capabilities of modern graphics hardware, the proposed algorithms are capable of modifying the illumination model and simulating illustrative motifs in real time. In order to enhance local details, which are useful to better perceive the shape and the surfaces of the volume, our first contribution is an algorithm that employs a common sharpening operator to modify the lighting applied. As a result, the overall contrast of the visualization is enhanced by brightening the salient features and darkening the deeper regions of the volume model. The enhancement of depth perception in Direct Volume Rendering is also covered in the thesis. To do this, we propose two algorithms to simulate ambient occlusion: a screen-space technique based on using depth information to estimate the amount of light occluded, and a view-independent method that uses the density values of the data set to estimate the occlusion. Additionally, depth perception is also enhanced by adding halos around the structures of interest. Maximum Intensity Projection images provide a good understanding of the high intensity features of the data, but lack any contextual information. In order to enhance the depth perception in such a case, we present a novel technique based on changing how intensity is accumulated. Furthermore, the perception of the spatial arrangement of the displayed structures is also enhanced by adding certain colour cues. The last contribution is a new manipulation tool designed for adding contextual information when cutting the volume. Based on traditional illustrative effects, this method allows the user to directly extrude structures from the cross-section of the cut. As a result, the clipped structures are displayed at different heights, preserving the information needed to correctly perceive them.Debido a la naturaleza de los datos científicos, visualizarlos correctamente puede ser una tarea complicada, pero crucial para interpretarlos de forma adecuada. Cuando se trabaja con modelos de volumen complejos, como es el caso de los modelos anatómicos, es importante generar imágenes precisas, ya que una mala interpretación de las mismas puede producir errores graves en el diagnóstico de enfermedades o en la planificación de operaciones quirúrgicas. En estos casos, mejorar la percepción de las zonas de interés, facilita la comprensión de la información inherente a los datos. Durante décadas, los investigadores se han centrado en el desarrollo de técnicas para mejorar la visualización de datos volumétricos. Por ejemplo, los métodos que permiten definir buenas funciones de transferencia son clave, ya que éstas determinan cómo se clasifican los materiales. Otros ejemplos son las técnicas que simulan modelos de iluminación realista, que permiten percibir mejor la distribución espacial de los elementos del volumen, o bien los que imitan efectos ilustrativos, que proporcionan el nivel de abstracción necesario para interpretar correctamente los datos. El trabajo presentado en esta tesis se centra en mejorar la percepción de los elementos del volumen, ya sea modificando el modelo de iluminación aplicado en la visualización, o simulando efectos ilustrativos. Aprovechando la capacidad de cálculo de los nuevos procesadores gráficos, se describen un conjunto de algoritmos que permiten obtener los resultados en tiempo real. Para mejorar la percepción de detalles locales, proponemos modificar el modelo de iluminación utilizando una conocida herramienta de procesado de imágenes (unsharp masking). Iluminando aquellos detalles que sobresalen de las superficies y oscureciendo las zonas profundas, se mejora el contraste local de la imagen, con lo que se consigue realzar los detalles de superficie. También se presentan diferentes técnicas para mejorar la percepción de la profundidad en Direct Volume Rendering. Concretamente, se propone modificar la iluminación teniendo en cuenta la oclusión ambiente de dos maneras diferentes: la primera utiliza los valores de profundidad en espacio imagen para calcular el factor de oclusión del entorno de cada pixel, mientras que la segunda utiliza los valores de densidad del volumen para aproximar dicha oclusión en cada vóxel. Además de estas dos técnicas, también se propone mejorar la percepción espacial y de la profundidad de ciertas estructuras mediante la generación de halos. La técnica conocida como Maximum Intensity Projection (MIP) permite visualizar los elementos de mayor intensidad del volumen, pero no aporta ningún tipo de información contextual. Para mejorar la percepción de la profundidad, proponemos una nueva técnica basada en cambiar la forma en la que se acumula la intensidad en MIP. También se describe un esquema de color para mejorar la percepción espacial de los elementos visualizados. La última contribución de la tesis es una herramienta de manipulación directa de los datos, que permite preservar la información contextual cuando se realizan cortes en el modelo de volumen. Basada en técnicas ilustrativas tradicionales, esta técnica permite al usuario estirar las estructuras visibles en las secciones de los cortes. Como resultado, las estructuras de interés se visualizan a diferentes alturas sobre la sección, lo que permite al observador percibirlas correctamente

Robust image steganography method suited for prining = Robustna steganografska metoda prilagođena procesu tiska

U ovoj doktorskoj dizertaciji prezentirana je robustna steganografska metoda razvijena i prilagođena za tisak. Osnovni cilj metode je pružanje zaštite od krivotvorenja ambalaže. Zaštita ambalaže postiže se umetanjem više bitova informacije u sliku pri enkoderu, a potom maskiranjem informacije kako bi ona bila nevidljiva ljudskom oku. Informacija se pri dekoderu detektira pomoću infracrvene kamere. Preliminarna istraživanja pokazala su da u relevantnoj literaturi nedostaje metoda razvijenih za domenu tiska. Razlog za takav nedostatak jest činjenica da razvijanje steganografskih metoda za tisak zahtjeva veću količinu resursa i materijala, u odnosu na razvijanje sličnih domena za digitalnu domenu. Također, metode za tisak često zahtijevaju višu razinu kompleksnosti, budući da se tijekom reprodukcije pojavljuju razni oblici procesiranja koji mogu kompromitirati informaciju u slici [1]. Da bi se sačuvala skrivena informacija, metoda mora biti otporna na procesiranje koje se događa tijekom reprodukcije. Kako bi se postigla visoka razina otpornosti, informacija se može umetnuti unutar frekvencijske domene slike [2], [3]. Frekvencijskoj domeni slike možemo pristupiti pomoću matematičkih transformacija. Najčešće se koriste diskretna kosinusna transformacija (DCT), diskretna wavelet transformacija (DWT) i diskretna Fourierova transformacija (DFT) [2], [4]. Korištenje svake od navedenih transformacija ima određene prednosti i nedostatke, ovisno o kontekstu razvijanja metode [5]. Za metode prilagođene procesu tiska, diskretna Fourierova transformacija je optimalan odabir, budući da metode bazirane na DFT-u pružaju otpornost na geometrijske transformacije koje se događaju tijekom reprodukcije [5], [6]. U ovom istraživanju korištene su slike u cmyk prostoru boja. Svaka slika najprije je podijeljena u blokove, a umetanje informacije vrši se za svaki blok pojedinačno. Pomoću DFT-a, ???? kanal slikovnog bloka se transformira u frekvencijsku domenu, gdje se vrši umetanje informacije. Akromatska zamjena koristi se za maskiranje vidljivih artefakata nastalih prilikom umetanja informacije. Primjeri uspješnog korištenja akromatske zamjene za maskiranje artefakata mogu se pronaći u [7] i [8]. Nakon umetanja informacije u svaki slikovni blok, blokovi se ponovno spajaju u jednu, jedinstvenu sliku. Akromatska zamjena tada mijenja vrijednosti c, m i y kanala slike, dok kanal k, u kojemu se nalazi umetnuta informacija, ostaje nepromijenjen. Time nakon maskiranja akromatskom zamjenom označena slika posjeduje ista vizualna svojstva kao i slika prije označavanja. U eksperimentalnom dijelu rada koristi se 1000 slika u cmyk prostoru boja. U digitalnom okruženju provedeno je istraživanje otpornosti metode na slikovne napade specifične za reprodukcijski proces - skaliranje, blur, šum, rotaciju i kompresiju. Također, provedeno je istraživanje otpornosti metode na reprodukcijski proces, koristeći tiskane uzorke. Objektivna metrika bit error rate (BER) korištena je za evaluaciju. Mogućnost optimizacije metode testirala se procesiranjem slike (unsharp filter) i korištenjem error correction kodova (ECC). Provedeno je istraživanje kvalitete slike nakon umetanja informacije. Za evaluaciju su korištene objektivne metrike peak signal to noise ratio (PSNR) i structural similarity index measure (SSIM). PSNR i SSIM su tzv. full-reference metrike. Drugim riječima, potrebne su i neoznačena i označena slika istovremeno, kako bi se mogla utvrditi razina sličnosti između slika [9], [10]. Subjektivna analiza provedena je na 36 ispitanika, koristeći ukupno 144 uzorka slika. Ispitanici su ocijenjivali vidljivost artefakata na skali od nula (nevidljivo) do tri (vrlo vidljivo). Rezultati pokazuju da metoda posjeduje visoku razinu otpornosti na reprodukcijski proces. Također, metoda se uistinu optimizirala korištenjem unsharp filtera i ECC-a. Kvaliteta slike ostaje visoka bez obzira na umetanje informacije, što su potvrdili rezultati eksperimenata s objektivnim metrikama i subjektivna analiza

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities

Intensity inhomogeneities in images constitute a considerable challenge in image segmentation. In this paper we propose a novel biconvex variational model to tackle this task. We combine a total variation approach for multi class segmentation with a multiplicative model to handle the inhomogeneities. Our method assumes that the image intensity is the product of a smoothly varying part and a component which resembles important image structures such as edges. Therefore, we penalize in addition to the total variation of the label assignment matrix a quadratic difference term to cope with the smoothly varying factor. A critical point of our biconvex functional is computed by a modified proximal alternating linearized minimization method (PALM). We show that the assumptions for the convergence of the algorithm are fulfilled by our model. Various numerical examples demonstrate the very good performance of our method. Particular attention is paid to the segmentation of 3D FIB tomographical images which was indeed the motivation of our work

Visibility Recovery on Images Acquired in Attenuating Media. Application to Underwater, Fog, and Mammographic Imaging

When acquired in attenuating media, digital images often suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasantness for the user. In these cases, mathematical image processing reveals itself as an ideal tool to recover some of the information lost during the degradation process. In this dissertation, we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fog removal and mammographic image processing. In the case of digital mammograms, X-ray beams traverse human tissue, and electronic detectors capture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces lowcontrasted images in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility. For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges, in this dissertation we develop new methodologies that rely on: a) physical models of the observed degradation, and b) the calculus of variations. Equipped with this powerful machinery, we design novel theoretical and computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energies are composed of different integral terms that are simultaneously minimized by means of efficient numerical schemes, producing a clean, visually-pleasant and useful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validate our methods, confirming that the developed techniques outperform other existing approaches in the literature