35 accepted papers
We propose a frequency-aware perceptual optimization framework for low-complexity image compression, realized as a **Re**alism-enhanced **Re**gion-based **I**mplicit **C**odec (Re2IC). Re2IC models visual perception via saliency-guided region partitioning and local–global perceptual modulation. To e…
We study zero-shot inverse problems, where a clean signal is recovered from a single degraded observation without external training data. Contrary to the common belief that such problems require highly complex models, we show that a lightweight neural network, when combined with entropy and complexi…
We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce the Score Anisotropy Directions (SADs), architecture-dependent directions that reveal how different networks preferentially capture data structure. Our a…
We introduce and validate the lottery codec hypothesis, which states that untrained subnetworks within randomly initialized networks can serve as synthesis networks for overfitted image compression, achieving rate-distortion (RD) performance comparable to trained networks. This hypothesis leads to a…
Diffusion models have transformed image synthesis through iterative denoising, by defining trajectories from noise to coherent data. While their capabilities are widely celebrated, a critical challenge remains unaddressed: ensuring responsible use by verifying whether an image originates from a mode…
Joint source-channel coding schemes based on deep neural networks (DeepJSCC) have recently achieved remarkable performance for wireless image transmission. However, these methods usually focus only on the distortion of the reconstructed signal at the receiver side with respect to the source at the t…
Single image reflection removal (SIRR) problem can be interpreted as a canonical blind source separation problem and is highly ill-posed. A parameter effective, fast learning and interpretable reflection removal algorithm is essential for many vision analysis applications. In this paper, we propose…
We present a signal driven multi-channel time encoding system for sampling signals with finite rate of innovation (FRI). The system produces samples in the form of finite differences from which the input signal can be exactly reconstructed. The use of finite differences allows diversity of TEM param…
Macro X-ray fluorescence (MA-XRF) scanning is commonly used to non-invasively analyse Old Master paintings by mapping the distribution of the chemical elements present in the artworks. The visual quality of the element distribution maps is very important for characterising the materials and understa…
Event cameras produce streams of events with high temporal resolution which do not suffer from motion blur. Current deep networks achieve high-quality video reconstruction from events, but most of them are large and difficult to interpret. In this work, we present a solution to this problem by syste…
In this paper, we consider the 2D tomography problem for a finite number of point sources, where the line integral projections are taken at unknown angles. We address the problem of recovering the point sources and estimating the projection angles. Using the property of the Radon transform of a poin…
We study privacy-aware communication over a wiretap channel using end-to-end learning. Alice wants to transmit a source signal to Bob over a binary symmetric channel, while passive eavesdropper Eve tries to infer some sensitive attribute of Alice’s source based on its overheard signal. Since we usua…
We consider a user releasing her data containing some personal information in return of a service. We model user’s personal information as two correlated random variables, one of them, called the secret variable, is to be kept private, while the other, called the useful variable, is to be disclosed…
Time encoding of continuous time signals is an alternative to classical sampling paradigms. The signal is encoded in the timing of output samples rather than their amplitudes. Of particular interest are integrate-and-fire time encoding machines (IF-TEM) for sampling signals with finite rate of innov…
Light-field microscopes are able to capture spatial and angular information of incident light rays. This allows reconstructing 3D locations of neurons from a single snap-shot. In this work, we propose a model-inspired deep learning approach to perform fast and robust 3D localization of sources using…
We consider physical fields induced by a finite number of instantaneous diffusion sources, which we sample using a mobile sensor, along unknown trajectories composed of multiple linear segments. We address the problem of estimating the sources, as well as the trajectory of the mobile sensor. Within…
Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a stream of Diracs. The task of reconstructing FRI signals from discrete samples is often transformed into a spectral est…
Macro X-Ray Fluorescence (XRF) scanning is an increasingly widely used imaging technique for the non-invasive detection and mapping of chemical elements in Old Master paintings. Existing approaches for XRF signal analysis require varying degrees of expert user input. They are mainly based on peak fi…
We investigate the use of integrate-and-fire systems to efficiently sample classes of non-bandlimited signals such as bursts of spikes. The sampling in this case is based on storing some timing information about the signal, and no information about its amplitude. We demonstrate that perfect reconstr…
Light Field Microscopy (LFM) is a 3D imaging technique that captures volumetric information in a single snapshot. It is appealing in microscopy because of its simple implementation and the peculiarity that it is much faster than methods involving scanning. However, volume reconstruction for LFM suff…
We propose a deep dictionary model for single image super-resolution (SISR) made of multiple layers of analysis dictionaries interlaced with corresponding soft-thresholding operations and a single synthesis dictionary. In this paper, we introduce a novel method for learning analysis dictionary and t…
In this paper, we propose a novel deep neural network architecture for multi-modal image super-resolution (MISR). The architecture is based on a new joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency and to map them to a high-resolution version of one modality.…
We study the privacy-cost trade-off in a smart meter (SM) system with a renewable energy source (RES) and a finite-capacity rechargeable battery (RB). Privacy is measured by the mutual information rate between the energy demand and the energy received from the grid, where the latter also determines…
The last two decades have seen a renewed interest in sampling theory, which is concerned with the conversion of continuous-domain signals into discrete sequences. This conversion is traditionally achieved by recording the intensity of the signal at specified time instants. Alternatively, sampling ca…
In single image super-resolution (SISR), given a low-resolution (LR) image, one wishes to find a high-resolution (HR) version of it which is both accurate and photorealistic. Recently, it has been shown that there exists a fundamental tradeoff between low distortion and high perceptual quality, and…
Inspired by the recent success of deep neural network architectures and the recent effort to develop multi-layer sparse models, we propose a novel deep dictionary learning architecture which is optimized to address a specific regression task known as single image super-resolution. Contrary to other…
Learning based single image super resolution (SISR) methods have achieved notable results, however, they require large datasets for training, and may struggle when there is a mismatch between the testing and training data. To overcome these drawbacks, we propose an approach, named U - FRESH, which o…
Blur in images, caused by camera motion with an open shutter, is usually thought of as a problem. The algorithm described in this paper shows instead that it is possible to use the blur caused by the integration of light rays at different locations along a moving camera trajectory to extract informa…
Recently it has been shown that specific classes of non-bandlimited signals known as signals with finite rate of innovation (FRI) can be perfectly reconstructed by using appropriate sampling kernels and reconstruction schemes. The knowledge of the model order (i.e. the rate of innovation) is essenti…
ProSparse is a Prony's based method that solves the sparse representation problem of signals in the union of Fourier and canonical bases. By exploiting the structure of the dictionary, ProSparse is able to reconstruct sparse signals beyond the recovery bound of Basis Pursuit. We generalize this fram…
We present a novel algorithm - ProSparse Denoise - that can solve the sparsity recovery problem in the presence of noise when the dictionary is the union of Fourier and identity matrices. The algorithm is based on a proper use of Cadzow routine and Prony's method and exploits the duality of Fourier…
We present a framework for estimating non-localized sources of diffusion fields using spatiotemporal measurements of the field. Specifically in this contribution, we consider two non-localized source types: straight line and polygonal sources and assume that the induced field is monitored using a se…
The objective of this work is to consider sparse representations of certain classes of signals on circulant graphs, by introducing families of graph wavelets which possess vanishing (exponential) moment properties. In light of this, we propose a novel framework of sampling and perfect reconstruction…
In this contribution, we implement a fully distributed diffusion field estimation algorithm based on the use of average consensus schemes. We show that the field reconstruction problem is equivalent to estimating the sources of the field, and then derive an exact inversion formula for jointly recove…
The problem of finding the sparse representation of a signal has attracted a lot of attention over the past years. In particular, uniqueness conditions and reconstruction algorithms have been established by relaxing a non-convex optimisation problem. The finite rate of innovation (FRI) theory is an…