FIB-SEM (Focused Ion Beam-Scanning Electron Microscopy) is an imaging technique that allows 3D ultrastructural analysis of cells and tissues at the nanoscale. The acquired FIB-SEM data are highly noisy, which makes denoising an essential step prior to volume interpretation. Gaussian filtering is a standard method in the field because it is fast and straightforward. However, it tends to blur the biological features due to its linear nature that ignores the rapid changes of the structures throughout the volume. To address this issue, we have developed a new approach to structure-preserving noise reduction for FIB-SEM. It has abilities to locally adapt the filtering to the biological structures while taking advantage of the simplicity of Gaussian filtering. It uses the Optical Flow (OF) to estimate the variations of the structural features across the volume, so that they are compensated before the subsequent filtering with a Gaussian function. As demonstrated qualitatively and objectively with datasets from different samples and acquired under different conditions, our denoising approach outperforms the standard Gaussian filtering and is competitive with state-of-the-art methods in terms of noise reduction and preservation of the sharpness of the structures.

Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) allows three-dimensional ultrastructural analysis of cells and tissues at the nanoscale. The technique iteratively removes a section of the sample with a FIB and takes an SEM image from the exposed surface. The section thickness is usually higher than the image pixel size to reduce acquisition time, thus resulting in anisotropic resolution. In this work, we explore novel interpolation methods along the sectioning direction to produce isotropic resolution and facilitate proper interpretation of the FIB-SEM 3D volumes. Classical interpolation methods are usually applied in this context under the assumption that the changes through successive images are relatively smooth. However, the actual 3D arrangement of the structures in the sample may cause significant changes in the biological features between consecutive images of the FIB-SEM stacks. We have developed a novel interpolation strategy that accounts for this variation by using the Optical Flow (OF) to estimate it. As an intermediate stage, OF-compensated images are produced by aligning the spatial regions of the biological structures. Interpolated images are then generated from these OF-compensated images. The final isotropic stack is assembled by interleaving the interpolated images with the original images of the anisotropic stack. OF-driven and classical interpolation methods were compared using an objective assessment based on Pearson Correlation Coefficient (PCC) and a qualitative evaluation based on visual results, using public datasets and representative anisotropy conditions. The objective assessment demonstrated that the OF-driven interpolation always yields higher PCC values, with interpolated images closer to the ground truth. The qualitative evaluation corroborated those results and confirmed that classical interpolation may blur areas with substantial changes between consecutive images whereas OF-driven interpolation provides sharpness. We have developed an OF-driven interpolation approach to generating FIB-SEM stacks with isotropic resolution from experimental anisotropic data. It adapts to the rapid variation of the biological structures observed through the images of the FIB-SEM stack. Our approach outperforms classical interpolation and manages to produce sharp interpolated views in cases where there are significant changes between consecutive experimental images.

Nowadays, Smart Environments (SEs) enable the monitoring of people with physical disabilities by incorporating activity recognition. Thermal cameras are being incorporated as they preserve privacy. Some Deep Learning (DL) solutions use the pose of the users because it removes external noise. Although there are robust DL solutions in the visible spectrum, they fail in the thermal domain. Thus, we propose THPoseLite (Thermal Human Pose Lite), a Convolutional Neural Network (CNN) based on MobileNetV2 that extracts pose from Thermal Images (TIs). In a novel way, an auto-labeling approach has been developed. It includes a background removal using an optical flow estimator. It also integrates Blazepose (a pose estimator for Visible spectrum images (VSIs)) to obtain the poses in the pre-processed TIs. Results show that the pre-processing increases the percentage of detected poses by Blazepose from 19.55% to 76.85%. This allows the recording of Human Pose Estimation (HPE) datasets in the visible spectrum without requiring visible spectrum cameras or manually annotating datasets. Furthermore, THPoseLite has been embedded in an Internet of Things (IoT) device incorporating an edge Tensor Processing Unit (TPU) accelerator, which can process TIs recorded at 9 Frames Per Second (FPS) in real-time (12.28 FPS). It requires fewer than 6W of energy to run. It has been achieved using model quantization, decreasing the accuracy in estimating the poses by only 1%. The MSE of MobileNetV2 in test images is 35.48, obtaining accurate poses in 21% of the images that Blazepose is not able to detect any pose.

The JPEG2000 image coding system was created with the intention of superseding the original JPEG standard, using a novel wavelet-based method. The main advantage of JPEG2000 is the flexibility of its code-stream, which provides new functionality related to the interactive transmission of images. For this task, JPEG2000 uses the JPIP protocol, which enables real-time spatial random access while the retrieved image is progressively displayed (streaming). The standard also foresees the compression and transmission of sequences of images by repeating this approach for each image. In this framework, this paper presents the Continue data-flow control strategy, a JPIP-compliant solution for the interactive streaming of sequences of images that are transmitted over time-varying communication channels. In this context, the random fluctuation of the capacity of the transmission channel over the time forces the clients to prefetch a minimal amount of the code-stream of each image of the beginning of the transmitted sequence before the playback starts, and the server to decide, in real-time, which amount of the code-stream of each compressed image is going to be transmitted . The estimated channel capacity is performed by clients and the rate-control at the server is straightforward, resulting in a highly scalable image retrieval system. The experiments conducted in this study demonstrate that the proposed method keeps a constant playback frame-rate under severe variations of the channel capacity, even when short prefetch times are used.

The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm has been built using only Clifford+T gates for compatibility with error detection and correction codes. Because fault-tolerant implementation of T gates has a much higher cost than other quantum gates, our focus has been on reducing the number of these gates. This has allowed adding noise tolerance, computational cost reduction, and fault tolerance to the state-of-the-art dual-threshold segmentation circuits. Since the dual-threshold image segmentation involves the comparison operation, as part of this work we have implemented two full comparator circuits. These circuits optimize the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature.