Publications

Over time, crack pattern (craquelure) inevitably develops in paintings as a sign of their ageing, sometimes accompanied by larger losses of paint (lacunas). In restoration treatments, cracks are typically not filled in, and virtual restoration is often the only option to "reverse" the ageing of paintings, simulating their original appearance. Moreover, virtual restoration can serve as an important supporting step in decision making during the ...

Band selection, which removes irrelevant bands from hyperspectral images (HSIs) and keeps essential spectral information contained in a relatively few bands, allows huge savings in data storage, computation time, and imaging hardware. In this article, we propose a novel structural subspace clustering (STSC) method for hyperspectral band selection, which leverages the self-representation property of data and structural prior information to learn ...

Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input HSI data as a dictionary of atoms, in terms of which all the data samples are linearly represented. This leads to highly redundant dictionaries of huge size and the computational complexity of the resulting optimization problems becomes prohibitive for large-scale data. ...

Convolutional neural network (CNN) has been widely applied in hyperspectral image (HSI) classification exhibiting excellent performance. Weak generalization of CNN models to different datasets is a common issue in this domain largely because of limited amount of labeled training samples. In this article, we propose a shape fully group convolutional neural network (FGCNN) method that integrates cascades of shuffled group convolutions tailored to ...

Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval. In this paper, we focus on learning local image descriptors in an unsupervised way, using autoencoders and variational autoencoders. We perform a thorough comparative analysis of these two approaches along with an in-depth analysis of the most relevant hyperparameters to guide their ...

Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as hyperspectral image (HSI), while, nowadays, multisensor data are being routinely acquired. In this article, we propose a multiview subspace clustering model that exploits effectively the rich information ...

Recent progress in spectral classification is largely attributed to the use of convolutional neural networks (CNN).While a variety of successful architectures have been proposed, they all extract spectral features from various portions of adjacent spectral bands. In this paper, we take a different approach and develop a deep spectral feature fusion method, which extracts both local and interlocal spectral features, capturing thus also the ...

Block matching is a fundamental tool to search blocks (patches) similar or identical to a given query in image processing. Generally, a full search (FS) algorithm is the most accurate but requires vast computation especially in multichannel images, where the data volume is increasing due to higher definition and more channels. In this paper, we present a fast FS-equivalent algorithm using orthonormal tree-structured Haar transform (OTSHT) for ...

Many image and signal processing problems benefit from quaternion based models, due to their property of processing different features simultaneously. Recently the quaternion algebra model has been combined with the dictionary learning and sparse representation models. This led to solving versatile optimization problems over the quaternion algebra. Since the quaternions form a noncommutative algebra, calculation of the gradient of the quaternion ...

In this paper, we propose an efficient method for learning a local image descriptor and its inversion function using a modified version of a variational autoencoder (VAE) - a U+03B2-VAE. We examine different values of U+03B2 in the loss function of the U+03B2-VAE to find an optimal balance between incentivising the similarities between input patches to be preserved in latent space, and ensuring good reconstruction of the patches from their ...