We introduce a structure-aware hybrid classical–quantum framework for discrete binary tomography from very few projections that enables practical quantum speedups through an explicit reduction of the problem’s combinatorial complexity. Rather than directly applying quantum algorithms to the full reconstruction task, we reshape the reconstruction problem by combining continuous relaxation and constraint propagation to deterministically fix the majority of pixel values, thereby isolating a small uncertain core. This core captures the residual combinatorial structure and has size k = O(n) for n × n images, reducing the effective search space from \(2^{n^2}\) to 2k, where k = O(n) and typically k ≪ n2 in practice. This reduced formulation is inherently compatible with quantum search. In particular, the refinement phase can be mapped to a search over {0, 1}k, enabling quadratic speedups via Grover’s algorithm, as well as more refined quantum backtracking strategies such as Montanaro’s algorithm. Experimental results on four projections from binary images up to 512 × 512 pixels show that the classical component alone achieves near-linear scaling and high reconstruction accuracy (above \(99.5\%\)), significantly outperforming DART and remaining competitive with ILP approaches. Importantly, the combinatorial refinement is both sparse and selective, activating primarily on difficult instances and exploring only a small fraction of the reduced search space. These findings demonstrate that the proposed decomposition acts as an effective structure-extraction layer, bridging classical reconstruction and quantum search, and providing a concrete pathway toward scalable and practically relevant quantum-enhanced discrete tomography.

In this paper we introduce a dataset of environmental information obtained via indoor and outdoor sensors deployed in the SMART Infrastructure Facility of the University of Wollongong (Australia). The acquired dataset is also made open-sourced along with this paper. We also propose a novel approach based on an evolutionary algorithm to determine pairs of correlated sensors. We compare our approach with three other standard techniques on the same dataset: on average, the accuracy of the evolutionary method is about 62,92%. We also evaluate the computational time, assessing the suitability of the proposed pipeline for real-time applications.

Retinal diseases such as retinopathy of prematurity (ROP), diabetic and hypertensive retinopathy present several deformities of fundus oculi which can be analyzed both during screening and monitoring such as the increase of tortuosity, lesions of tissues, exudates and hemorrhages. In particular, one of the first morphological changes of vessel structures is the increase of tortuosity. The aim of this work is the enhancement and the detection of the principal characteristics in retinal image by exploiting a non-supervised and automated methodology. With respect to the well-known image analysis through Gabor or Gaussian filters, our approach uses a filter bank that resembles the “à trous” wavelet algorithm. In this contribution we show a particular approach to speed-up the computing time. This methodology rotates the kernels and it is a fast enough to extract information useful to assess vessel tortuosity and to segment (not considered explicitly in this paper) retinal images. Furthermore, we compare on the public databases DRIVE and DIARETDB0 our output images against the SCIRD-TS algorithm, which is considered as one of the most effective supervised methods for the detection of retinal thin structures

We aim at describing a non-parametric approach to evaluate blood cells velocity in oral capillascopic videos. The proposed methodology is based on the application of standard optical flow algorithms and it is part of a general environment to support during the diagnostic process for evaluating peripheral microcirculation in real time. We validated our approach versus handmade measurements provided by physicians. Results on real data pointed out that our system returns an output coherent to these latter observations.

Nowadays the measurement of the nuchal translucency thickness is being used as part of routine ultrasound scanning during the end of the first trimester of pregnancy, for the screening of chromosomal defects, as trisomy 21. Currently, the measurement is being performed manually by physicians. The measurement can take a long time for being accomplished, needs to be performed by highly skilled operators, and is prone to errors. Semi-automated methods requires that the user manually selects a region of the image containing the nuchal translucency, procedure that is somewhat time consuming. In this paper we present a complete system prototype that is able to perform the measurement of the nuchal translucency thickness without any manual intervention from the operator, operating on the video stream coming out from the ultrasound machine.

Accurate retinal image registration is essential to track the evolution of eye-related diseases. We propose a semiautomatic method based on features relying upon retinal graphs for temporal registration of retinal images. The features represent straight lines connecting vascular landmarks on the retina vascular tree: bifurcations, branchings, crossings, end points. In the built retinal graph, one straight line between two vascular landmarks indicates that they are connected by a vascular segment in the original retinal image. The locations of the landmarks are manually extracted to avoid the information loss due to errors in a retinal vessels segmentation algorithms. A straight line model is designed to compute a similarity measure to quantify the line matching between images. From the set of matching lines, corresponding points are extracted and a global transformation is computed. The performance of the registration method is evaluated in the absence of ground truth using the cumulative inverse consistency error (CICE).

In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor representation in the olfactory bulb circuit. Specifically, we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances. We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments, through the modulation of synaptic weights during odor training. Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network, with the frequency of these synchronized spiking patterns also modulated by the sniff cycle.

A parallel version of a new automatic Harris-based corner detector is presented. A scheduler to dynamically and homogeneously distribute high computational workload on heterogeneous parallel architectures such as Grid systems has been implemented to speedup the whole procedure. Experimental results show the robustness of the underlying scheduler, which can be easily exploited in various automatic image analysis systems.

This paper introduces a new Harris-affine corner detector algorithm, that does not need parameters to locate corners in images, given an observation scale. Standard detectors require to fine tune the values of parameters which strictly depend on the particular input image. A quantitative comparison between our implementation and a standard Harris-affine implementation provides good results, showing that the proposed methodology is robust and accurate. The benchmark consists of public images used in literature for feature detection.

Indexing and retrieval methods based on the image content are required to effectively use information from the large repositories of digital images and videos currently available. Both global (colour, texture, motion, etc.) and local (object shape, etc.) features are needed to perform a reliable content based retrieval. We present a method for automatic extraction of global image features, like colour and motion parameters, and their use for data restriction in video database querying. Further retrieval is therefore accomplished, in a restricted set of images, by shape feature (skeleton, local symmetry moments, correlation, etc.) local search. The proposed indexing methodology has been developed and tested inside JACOB, a prototypal system for content-based video database querying.