Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used

Parallel Diffusion Models of Operator and Image for Blind Inverse Problems

Diffusion model-based inverse problem solvers have demonstrated state-of-the-art performance in cases where the forward operator is known (i.e. non-blind). However, the applicability of the method to blind inverse problems has yet to be explored. In this work, we show that we can indeed solve a family of blind inverse problems by constructing another diffusion prior for the forward operator. Specifically, parallel reverse diffusion guided by gradients from the intermediate stages enables joint optimization of both the forward operator parameters as well as the image, such that both are jointly estimated at the end of the parallel reverse diffusion procedure. We show the efficacy of our method on two representative tasks -- blind deblurring, and imaging through turbulence -- and show that our method yields state-of-the-art performance, while also being flexible to be applicable to general blind inverse problems when we know the functional forms.Comment: 25 pages, 13 figure

Data-Driven Image Restoration

Every day many images are taken by digital cameras, and people are demanding visually accurate and pleasing result. Noise and blur degrade images captured by modern cameras, and high-level vision tasks (such as segmentation, recognition, and tracking) require high-quality images. Therefore, image restoration specifically, image deblurring and image denoising is a critical preprocessing step. A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. Existing image deblurring techniques often rely on generic image priors that only help recover part of the frequency spectrum, such as the frequencies near the high-end. To this end, we pose the following specific questions: (i) Does class-specific information offer an advantage over existing generic priors for image quality restoration? (ii) If a class-specific prior exists, how should it be encoded into a deblurring framework to recover attenuated image frequencies? Throughout this work, we devise a class-specific prior based on the band-pass filter responses and incorporate it into a deblurring strategy. Specifically, we show that the subspace of band-pass filtered images and their intensity distributions serve as useful priors for recovering image frequencies. Next, we present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms, our method selects clean image “support patches” similar to the noisy patch from an external database. We employ a content adaptive distribution model for each patch where we derive the parameters of the distribution from the support patches. Our objective function composed of a Gaussian fidelity term that imposes category specific information, and a low-rank term that encourages the similarity between the noisy and the support patches in a robust manner. Finally, we propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task. Firstly, each residual unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution layer of each module can observe the full receptive field of the first layer

Pupil-driven quantitative differential phase contrast imaging

In this research, we reveal the inborn but hitherto ignored properties of quantitative differential phase contrast (qDPC) imaging: the phase transfer function being an edge detection filter. Inspired by this, we highlighted the duality of qDPC between optics and pattern recognition, and propose a simple and effective qDPC reconstruction algorithm, termed Pupil-Driven qDPC (pd-qDPC), to facilitate the phase reconstruction quality for the family of qDPC-based phase reconstruction algorithms. We formed a new cost function in which modified L0-norm was used to represent the pupil-driven edge sparsity, and the qDPC convolution operator is duplicated in the data fidelity term to achieve automatic background removal. Further, we developed the iterative reweighted soft-threshold algorithms based on split Bregman method to solve this modified L0-norm problem. We tested pd-qDPC on both simulated and experimental data and compare against state-of-the-art (SOTA) methods including L2-norm, total variation regularization (TV-qDPC), isotropic-qDPC, and Retinex qDPC algorithms. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, in which it significantly increases the experimental robustness while maintaining the data fidelity. In general, the pd-qDPC enables the high-quality qDPC reconstruction without any modification of the optical system. It simplifies the system complexity and benefits the qDPC community and beyond including but not limited to cell segmentation and PTF learning based on the edge filtering property

Dictionary optimization for representing sparse signals using Rank-One Atom Decomposition (ROAD)

Dictionary learning has attracted growing research interest during recent years. As it is a bilinear inverse problem, one typical way to address this problem is to iteratively alternate between two stages: sparse coding and dictionary update. The general principle of the alternating approach is to fix one variable and optimize the other one. Unfortunately, for the alternating method, an ill-conditioned dictionary in the training process may not only introduce numerical instability but also trap the overall training process towards a singular point. Moreover, it leads to difficulty in analyzing its convergence, and few dictionary learning algorithms have been proved to have global convergence. For the other bilinear inverse problems, such as short-and-sparse deconvolution (SaSD) and convolutional dictionary learning (CDL), the alternating method is still a popular choice. As these bilinear inverse problems are also ill-posed and complicated, they are tricky to handle. Additional inner iterative methods are usually required for both of the updating stages, which aggravates the difficulty of analyzing the convergence of the whole learning process. It is also challenging to determine the number of iterations for each stage, as over-tuning any stage will trap the whole process into a local minimum that is far from the ground truth. To mitigate the issues resulting from the alternating method, this thesis proposes a novel algorithm termed rank-one atom decomposition (ROAD), which intends to recast a bilinear inverse problem into an optimization problem with respect to a single variable, that is, a set of rank-one matrices. Therefore, the resulting algorithm is one stage, which minimizes the sparsity of the coefficients while keeping the data consistency constraint throughout the whole learning process. Inspired by recent advances in applying the alternating direction method of multipliers (ADMM) to nonconvex nonsmooth problems, an ADMM solver is adopted to address ROAD problems, and a lower bound of the penalty parameter is derived to guarantee a convergence in the augmented Lagrangian despite nonconvexity of the optimization formulation. Compared to two-stage dictionary learning methods, ROAD simplifies the learning process, eases the difficulty of analyzing convergence, and avoids the singular point issue. From a practical point of view, ROAD reduces the number of tuning parameters required in other benchmark algorithms. Numerical tests reveal that ROAD outperforms other benchmark algorithms in both synthetic data tests and single image super-resolution applications. In addition to dictionary learning, the ROAD formulation can also be extended to solve the SaSD and CDL problems. ROAD can still be employed to recast these problems into a one-variable optimization problem. Numerical tests illustrate that ROAD has better performance in estimating convolutional kernels compared to the latest SaSD and CDL algorithms.Open Acces

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

This work was sponsored in part by the Agencia Es-tatal de Investigacion under project PID2019-105142RB-C22/AEI/10.13039/50110 0 011033, Junta de Andalucia under project PY20_00286,and the work by Fernando Perez-Bueno was spon-sored by Ministerio de Economia, Industria y Competitividad un-der FPI contract BES-2017-081584. Funding for open access charge: Universidad de Granada/CBUA.Background and Objective: Color variations in digital histopathology severely impact the performance of computer-aided diagnosis systems. They are due to differences in the staining process and acquisition system, among other reasons. Blind color deconvolution techniques separate multi-stained images into single stained bands which, once normalized, can be used to eliminate these negative color variations and improve the performance of machine learning tasks. Methods: In this work, we decompose the observed RGB image in its hematoxylin and eosin components. We apply Bayesian modeling and inference based on the use of Super Gaussian sparse priors for each stain together with prior closeness to a given reference color-vector matrix. The hematoxylin and eosin components are then used for image normalization and classification of histological images. The proposed framework is tested on stain separation, image normalization, and cancer classification problems. The results are measured using the peak signal to noise ratio, normalized median intensity and the area under ROC curve on five different databases. Results: The obtained results show the superiority of our approach to current state-of-the-art blind color deconvolution techniques. In particular, the fidelity to the tissue improves 1,27 dB in mean PSNR. The normalized median intensity shows a good normalization quality of the proposed approach on the tested datasets. Finally, in cancer classification experiments the area under the ROC curve improves from 0.9491 to 0.9656 and from 0.9279 to 0.9541 on Camelyon-16 and Camelyon-17, respectively, when the original and processed images are used. Furthermore, these figures of merits are better than those obtained by the methods compared with. Conclusions: The proposed framework for blind color deconvolution, normalization and classification of images guarantees fidelity to the tissue structure and can be used both for normalization and classification. In addition, color deconvolution enables the use of the optical density space for classification, which improves the classification performance.Agencia Es-tatal de Investigacion PID2019-105142RB-C22/AEI/10.13039/50110 0 011033Junta de Andalucia PY20_00286Ministerio de Economia, Industria y Competitividad under FPI BES-2017-081584Universidad de Granada/CBU

Restoration and Domain Adaptation for Unconstrained Face Recognition

Face recognition (FR) has received great attention and tremendous progress has been made during the past two decades. While FR at close range under controlled acquisition conditions has achieved a high level of performance, FR at a distance under unconstrained environment remains a largely unsolved problem. This is because images collected from a distance usually suffer from blur, poor illumination, pose variation etc. In this dissertation, we present models and algorithms to compensate for these variations to improve the performance for FR at a distance. Blur is a common factor contributing to the degradation of images collected from a distance, e.g., defocus blur due to long range acquisition, motion blur due to movement of subjects. For this purpose, we study the image deconvolution problem. This is an ill-posed problem, and solutions are usually obtained by exploiting prior information of desired output image to reduce ambiguity, typically through the Bayesian framework. In this dissertation, we consider the role of an example driven manifold prior to address the deconvolution problem. Specifically, we incorporate unlabeled image data of the object class in the form of a patch manifold to effectively regularize the inverse problem. We propose both parametric and non-parametric approaches to implicitly estimate the manifold prior from the given unlabeled data. Extensive experiments show that our method performs better than many competitive image deconvolution methods. More often, variations from the collected images at a distance are difficult to address through physical models of individual degradations. For this problem, we utilize domain adaptation methods to adapt recognition systems to the test data. Domain adaptation addresses the problem where data instances of a source domain have different distributions from that of a target domain. We focus on the unsupervised domain adaptation problem where labeled data are not available in the target domain. We propose to interpolate subspaces through dictionary learning to link the source and target domains. These subspaces are able to capture the intrinsic domain shift and form a shared feature representation for cross domain recognition. Experimental results on publicly available datasets demonstrate the effectiveness of our approach for face recognition across pose, blur and illumination variations, and cross dataset object classification. Most existing domain adaptation methods assume homogeneous source domain which is usually modeled by a single subspace. Yet in practice, oftentimes we are given mixed source data with different inner characteristics. Modeling these source data as a single domain would potentially deteriorate the adaptation performance, as the adaptation procedure needs to account for the large within class variations in the source domain. For this problem, we propose two approaches to mitigate the heterogeneity in source data. We first present an approach for selecting a subset of source samples which is more similar to the target domain to avoid negative knowledge transfer. We then consider the scenario that the heterogenous source data are due to multiple latent domains. For this purpose, we derive a domain clustering framework to recover the latent domains for improved adaptation. Moreover, we formulate submodular objective functions which can be solved by an efficient greedy method. Experimental results show that our approaches compare favorably with the state-of-the-art