On Tuesday, May 28th, 2024, 14h, we’ll be glad to welcome Ulugbek Kamilov from Washington University, St. Louis, USA, for a joint ISPGroup / LINMA2120 Seminar entitled “Computational Imaging: Restoration Deep Networks as Implicit Priors.” More information here and here.
Welcome to ISPGroup!
The Image and Signal Processing Group of UCLouvain, ICTEAM, Belgium, whose members are listed here, is active in many theories, models, techniques, codes, datasets, and codecs related to signal and image processing in a broad sense.
For instance, the ISPGroup researchers work on signal acquisition, compression and streaming; machine and deep learning for computer vision; (multiple) object tracking; content-based data retrieval; biomedical signal and medical image processing; compressed sensing and inverse problem solving, compputational imaging; hyperspectral imaging; tomographic methods; sparse signal representation; data restoration, … and many other topics described in some of these posts, these publications, and in this visual—and clickable—word cloud:
Apr 26, 2024
Dec 23, 2023
New postdoc position opened in the research group of Prof. Laurent Jacques to work on the QuadSense project, a new project funded by the Belgian Fund for Scientific Research - FNRS.
More information (application procedure, project topic, …) → check here ←
Feb 10, 2023
Olivier Leblanc got the best contribution award at the Biomedical and Astronomical Signal Processing conference (BASP'23) for his work entitled “Interferometric Lensless Imaging – Rank-one Projections of Image Frequencies with Speckle Illuminations.”
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Signal Processing after quadratic sketching
Sketching is a commonly used technique in signal processing. In this short piece we describe how to estimate functions of a given signal based solely on its sketch and without explicit reconstruction.Nov 20, 2023 -
Body Part-Based Representation Learning for Occluded Person Re-Identification
BPBReID, a part-based re-identification method using body part feature representations to compute to similarity between two person images.Jul 3, 2023 -
On the Importance of Denoising When Learning to Compress Images
Image noise is not compressible nor desirable. Image compression models can be trained for joint denoising and compression resulting in a much better rate-distortion when encoding images which contain noise (and no adverse effect on clean images). Joint models outperform running a dedicated denoiser prior to compression without any of the complexity associated with denoising.Jan 31, 2023