Dr KÚvin Degraux
Remark: This page is not a homepage! It lists only the activities of the ISPGroup member mentioned above (e.g., his/her research, project, seminars, ...)
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.
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.
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.
- Computational Sensing Strategies for Low-Complexity Signal Models. Principal investigator: Prof. Laurent Jacques