Phase unwrapping for phase imaging using the plug-and-play proximal algorithm.

Phase unwrapping (PU) is essential for various scientific optical applications. This process aims to estimate continuous phase values from acquired wrapped values, which are limited to the interval (-π,π]. However, the PU process can be challenging due to factors such as insufficient sampling, measurement errors, and inadequate equipment calibration, which can introduce excessive noise and unexpected phase discontinuities. This paper presents a robust iterative method based on the plug-and-play (PnP) proximal algorithm to unwrap two-dimensional phase values while simultaneously removing noise at each iteration. Using a least-squares formulation based on local phase differences and reformulating it as a partially differentiable equation, it is possible to employ the fast cosine transform to obtain a closed-form solution for one of the subproblems within the PnP framework. As a result, reliable phase reconstruction can be achieved even in scenarios with extremely high noise levels.

Computational spectral imaging: a contemporary overview.

Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral images (SIs) allow the identification of objects, crops, and materials in the scene through their spectral behavior. Since most spectral optical systems can only employ 1D or maximum 2D sensors, it is challenging to directly acquire 3D information from available commercial sensors. As an alternative, computational spectral imaging (CSI) has emerged as a sensing tool where 3D data can be obtained using 2D encoded projections. Then, a computational recovery process must be employed to retrieve the SI. CSI enables the development of snapshot optical systems that reduce acquisition time and provide low computational storage costs compared with conventional scanning systems. Recent advances in deep learning (DL) have allowed the design of data-driven CSI to improve the SI reconstruction or, even more, perform high-level tasks such as classification, unmixing, or anomaly detection directly from 2D encoded projections. This work summarizes the advances in CSI, starting with SI and its relevance and continuing with the most relevant compressive spectral optical systems. Then, CSI with DL will be introduced, as well as the recent advances in combining the physical optical design with computational DL algorithms to solve high-level tasks.

Calibration reinforcement regularizations for optimized snapshot spectral imaging.

Optical coding is a fundamental tool in snapshot computational spectral imaging for capturing encoded scenes that are then decoded by solving an inverse problem. Optical encoding design is crucial, as it determines the invertibility properties of the system sensing matrix. To ensure a realistic design, the optical mathematical forward model must match the physical sensing. However, stochastic variations related to non-ideal characteristics of the implementation exist; therefore, these variables are not known a priori and have to be calibrated in the laboratory setup. Thus, the optical encoding design leads to suboptimal performance in practice, even if an exhaustive calibration process is carried out. This work proposes an algorithm to speed up the reconstruction process in a snapshot computational spectral imaging, in which theoretically optimized coding design is distorted by the implementation process. Specifically, two regularizers are proposed that perform the gradient algorithm iterations of the distorted calibrated system in the direction of the originally, theoretically optimized system. We illustrate the benefits of the reinforcement regularizers for several state-of-the-art recovery algorithms. For a given lower bound performance, the algorithm converges in fewer iterations due to the effect of the regularizers. Simulation results show an improvement of up to 2.5 dB of peak signal-to-noise ratio (PSNR) when fixing the number of iterations. Furthermore, the required number of iterations reduces up to 50% when the proposed regularizers are included to obtain a desired performance quality. Finally, the effectiveness of the proposed reinforcement regularizations was evaluated in a test-bed implementation, where a better spectral reconstruction was evidenced when compared with a non-regularized system's reconstruction.

JR2net: a joint non-linear representation and recovery network for compressive spectral imaging.

Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear mapping from compressed measurements to the spectral image. For instance, the deep spectral prior approach uses a convolutional autoencoder (CAE) network in the optimization algorithm to recover the spectral image by using a non-linear representation. However, the CAE training is detached from the recovery problem, which does not guarantee optimal representation of the spectral images for the CSI problem. This work proposes a joint non-linear representation and recovery network (JR2net), linking the representation and recovery task into a single optimization problem. JR2net consists of an optimization-inspired network following an alternating direction method of multipliers (ADMM) formulation that learns a non-linear low-dimensional representation and simultaneously performs the spectral image recovery, trained via the end-to-end approach. Experimental results show the superiority of the proposed method with improvements up to 2.57 dB in peak signal-to-noise ratio (PSNR) and performance around 2000 times faster than state-of-the-art methods.

Compressive spectral image reconstruction using deep prior and low-rank tensor representation.

Compressive spectral imaging (CSI) has emerged as an alternative spectral image acquisition technology, which reduces the number of measurements at the cost of requiring a recovery process. In general, the reconstruction methods are based on handcrafted priors used as regularizers in optimization algorithms or recent deep neural networks employed as an image generator to learn a non-linear mapping from the low-dimensional compressed measurements to the image space. However, these deep learning methods need many spectral images to obtain good performance. In this work, a deep recovery framework for CSI without training data is presented. The proposed method is based on the fact that the structure of some deep neural networks and an appropriated low-dimensional structure are sufficient to impose a structure of the underlying spectral image from CSI. We analyzed the low-dimensional structure via the Tucker representation, modeled in the first net layer. The proposed scheme is obtained by minimizing the ${\ell _2}$-norm distance between the compressive measurements and the predicted measurements, and the desired recovered spectral image is formed just before the forward operator. Simulated and experimental results verify the effectiveness of the proposed method for the coded aperture snapshot spectral imaging.

Coupled deep learning coded aperture design for compressive image classification.

A coupled deep learning approach for coded aperture design and single-pixel measurements classification is proposed. A whole neural network is trained to simultaneously optimize the binary sensing matrix of a single-pixel camera (SPC) and the parameters of a classification network, considering the constraints imposed by the compressive architecture. Then, new single-pixel measurements can be acquired and classified with the learned parameters. This method avoids the reconstruction process while maintaining classification reliability. In particular, two network architectures were proposed, one learns re-projected measurements to the image size, and the other extracts small features directly from the compressive measurements. They were simulated using two image data sets and a test-bed implementation. The first network beats in around 10% the accuracy reached by the state-of-the-art methods. A 2x increase in computing time is achieved with the second proposed net.

SPUD: simultaneous phase unwrapping and denoising algorithm for phase imaging.

Recent methods for phase unwrapping in the presence of noise include denoising algorithms to filter out noise as a preprocessing stage. However, including a denoising stage increases the overall computational complexity resulting in long execution times. In this paper, we present a noniterative simultaneous phase unwrapping and denoising algorithm for phase imaging, referred to as SPUD. The proposed method relies on the least squares discrete cosine transform (DCT) solution for phase unwrapping with an additional sparsity constraint on the DCT coefficients of the unwrapped solution. Simulation results with different levels of noise and wrapped phase fringe density reveal the suitability of the proposed method for accurate phase unwrapping and restoration. When compared to the 2D windowed Fourier transform filter, SPUD performs better in terms of phase error and execution times. The processing of experimental data from synthetic aperture radar showed the capability for processing real images, including removing phase dislocations. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule.

Automated chronic wounds medical assessment and tracking framework based on deep learning.

Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.

Super-Resolution Phase Retrieval from Designed Coded Diffraction Patterns.

Super-resolution phase retrieval is an inverse problem that appears in diffractive optical imaging (DOI) and consists in estimating a high-resolution image from low-resolution phaseless measurements. DOI has three diffraction zones where the data can be acquired, known as near, middle, and far fields. Recent works have studied super-resolution phase retrieval under a setup that records coded diffraction patterns at the near and far fields. However, the attainable resolution of the image is mainly governed by the sensor characteristics, whose cost increases in proportion to the resolution. Also, these methodologies lack theoretical analysis. Hence, this work derives super-resolution models from low-resolution coded phaseless measurements at any diffraction zone that in contrast to prior contributions, the attainable resolution of the image is determined by the resolution of the coded aperture. For the proposed models, the existence of a unique solution (up to a global unimodular constant) is guaranteed with high probability, which can be increased by designing the coded aperture. Therefore, a strategy that designs the spatial distribution of the coded aperture is developed. Additionally, a super-resolution phase retrieval algorithm that minimizes a smoothed nonconvex least-squares objective function is proposed. The method first approximates the image by a spectral algorithm, which is then refined based upon a sequence of alternate steps. Simulation results show that the proposed algorithm overcomes state-of-the-art methods in reconstructing the high-resolution image. In addition, the reconstruction quality using designed coded apertures is higher than that of the non-designed ensembles.

Insights Into the Factors Influencing Student Motivation in Augmented Reality Learning Experiences in Vocational Education and Training.

Research on Augmented Reality (AR) in education has demonstrated that AR applications designed with diverse components boost student motivation in educational settings. However, most of the research conducted to date, does not define exactly what those components are and how these components positively affect student motivation. This study, therefore, attempts to identify some of the components that positively affect student motivation in mobile AR learning experiences to contribute to the design and development of motivational AR learning experiences for the Vocational Education and Training (VET) level of education. To identify these components, a research model constructed from the literature was empirically validated with data obtained from two sources: 35 students from four VET institutes interacting with an AR application for learning for a period of 20 days, and a self-report measure obtained from the Instructional Materials Motivation Survey (IMMS). We found that the following variables: use of scaffolding, real-time feedback, degree of success, time on-task and learning outcomes are positively correlated with the four dimensions of the ARCS model of motivation: Attention, Relevance, Confidence, and Satisfaction. Implications of these results are also described.

Características de las demandas por lesiones a nervios periféricos durante procedimientos invasivos. Vinculación de los anestesiólogos / Characteristics of the demands for injuries to peripheral nerves during invasive procedures. Entail of the Anestesiologist

Se realizó un estudio descriptivo del tipo serie de casos, sobre lesiones de nervios periféricos como complicación de intervenciones médicas invasivas, en 29 demandas por este resultado adverso a profesionales de la salud, asociados al Fondo Especial para Auxilio Solidario de Demandas (FEPASDE) de la Sociedad Colombiana de Anestesiología y Reanimación (SCARE), revisados por la División Científica entre los años 1999 a 2005. Entre las variables analizadas, se recopiló información sobre el tipo de procedimiento, nervios con mayor frecuencia de afectación, daños generados, factores predisponentes, secuelas presentadas y evidencia de errores médicos, agregando otras de interés para FEPASDE en relación con el riesgo de las diferentes especialidades y aspectos probatorios de los procesos. En los resultados se destaca la mayor frecuencia de demandas contra los anestesiólogos y la frecuente indeterminación de la causa exacta del daño, aunada con una baja evidencia de fallas de atención o errores médicos generadores del problema. Adicionalmente, la no-existencia de adecuada información al paciente se destacó como debilidad probatoria en la defensa de los profesionales. Los resultados permiten orientar hacia lo que la jurisprudencia colombiana ha mencionado para estos casos, ya que por la dificultad de establecer la verdadera causa del daño y la no evidencia de falla de atención, no se puede establecer responsabilidad al profesional de la salud.

La responsabilidad médico-legal en ortopedia / Physician-legal responsability in orthopedics

Dentro de la revisión por especialidades sobre el perfil caracteristico de las demandas de responsabilidad, se presenta a continuación la de ortopedia, basada en la revisión de una muestra de 21 casos, revisados por la división cientifica entre los años 2000 y 2001. Seguramente las condiciones especiales necesarias para el ejercicio de la ortopedia y la revisión de casos presentados nos ayudaran a tener una idea al respecto. Esperamos que estos datos suministrados puedan ayudar en la toma de actitudes y medidas que ayuden a controlar razonablemente los riesgos de estar abocados a enfrentar situaciones de responsabilidad profesional.