Image Processing
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Tone Mapping for X-Ray images
Inspecting X-ray images is an essential aspect for medical diagnosis and for nondestructive control of manufactured objects in the industrial field. However, X-ray images are characterized by a high dynamic range and a low contrast. Due to those characteristics, and the limitation of the human visual system, important aspects such as nodules, bones fractures, gas inclusions or other kind of defects and anomalies are difficult to identify for the human eyes.The challenge is therefore to reduce the image bit depth, while preserving subtle and fine-grained local variation in the image.Mar 4, 2020 -
Deep Learning for Anomaly Detection in Industrial Vision
In order to detect anomaly in an unsupervised scheme, an autoencoder is trained to reconstruct clean images out of defect-free images corrupted with synthetic noise. During inference an arbitrary (with or without anomaly) image is projected onto the normal space of images. The intensity of the residual map between the original image and its reconstruction estimates the likelihood of a region to be defective.Mar 2, 2020
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Online Convolutional Dictionary Learning for Multimodal Imaging:
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this work, we propose a new method that reconstructs multimodal images from their linear measurements by exploiting redundancies across different modalities. Our method combines a convolutional group-sparse representation of images with TV regularization for high-quality multimodal imaging. We develop an online algorithm that enables the unsupervised learning of convolutional dictionaries on large-scale datasets that are typical in such applications.Oct 23, 2017 -
Imaging the Brain Microstructure
Diffusion tensor imaging, the most common model of the diffusion signal, is unable to represent the signal arising from water molecules diffusing in different compartments such as multiple fascicles and the extra-axonal space. Multi-fascicle models overcome this limitation by providing a parametric representation for each compartment, allowing the development of new brain network modeling.Sep 19, 2017