ICTEAM > ISPGroup > Research@ISPGroup > SensingImaging

Sensing, Imaging and Rendering

What is the most efficient way to sense a signal or to image reality? How can the sensing stage already "compute" information in a way that "just the information we need" is captured? This is the general research topic that we address here. This covers fields like, inverse problem solving, compressed sensing and compressive imaging, data restoration techniques, hyperspectral and light field imaging, or virtual view point rendering.


Compressive Schlieren Deflectometry: This project concerns with the application of compressive sensing principles for characterizing transparent objects using schlieren deflectometry. This is an instance of real world applications of compressed sensing.

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.

Compressive Optical Deflectometric Tomography: The refractive index map allows the optical characterization of complex transparent materials such as optical fibers or intraocular lenses. This research topic addresses the problem of reconstructing the refractive index map of a transparent object from few amount of optical deflectometric measurements. We aim at developing a numerical reconstruction method which makes Optical Deflectometric Tomography compressive and robust to noise.

Compressed Sensing and High Resolution Quantization: Measurement quantization is a critical step in the design and in the dissemination of new technologies implementing the Compressed Sensing (CS) paradigm. Quantization is indeed mandatory for transmitting, storing and even processing any data sensed by a CS device.

Blind Image Deconvolution for Non-parametric PSF estimation in Solar Astronomy: This research proposes to estimate the point spread function (PSF) of an telescope from the observation of the transit of a celestial body (e.g., the Moon or Venus) in front of the Sun. This is achieved by solving a regularized blind deconvolution problem.

Bilinear and Biconvex Inverse Problems for Computational Sensing Systems: A research effort in the solution of blind calibration and deconvolution problems arising in compressive imaging.

Bridging 1-bit and High-Resolution Quantized Compressed Sensing with QIHT: In the framework of Quantized Compressed Sensing, we tried to bridge two extreme cases: 1-bit and high resolution quantization. The requirement of consistency of the reconstructed signal with quantized measurement led us to a new reconstruction algorithm called Quantized IHT (QIHT) that outperforms classical algorithms (IHT and BPDN) at low resolutions.

Fusion-based techniques to enhance images and videos: One of the most successful image fusion strategies is based on the Laplacian pyramid decomposition. In the context of multi-scale fusion, the Laplacian pyramid decomposition has recently been demonstrated to be effective for several challenging tasks to enhance images and videos.

Last updated February 02, 2017, at 03:47 PM