# compressive sensing

### Jointly low-rank and bisparse recovery: Questions and partial answers

Abstract: We investigate the problem of recovering jointly $r$-rank and $s$-bisparse matrices from as few linear measurements as possible, considering arbitrary measurements as well as rank-one measurements. In both cases, we show that $$m ≍ r s łn(en/s)$$ measurements make the recovery possible in theory, meaning via a nonpractical algorithm.

### Performance of Compressive Sensing for Hadamard-Haar Systems

Abstract: We study the problem of image recovery from subsampled Hadamard measurements using Haar wavelet sparsity prior. This problem is of interest in, e.g., computational imaging applications relying on optical multiplexing or single pixel imaging.

### Quantity over Quality: Dithered Quantization for Compressive Radar Systems

Abstract: In this paper, we investigate a trade-off between the number of radar observations (or measurements) and their resolution in the context of radar range estimation. To this end, we introduce a novel estimation scheme that can deal with strongly quantized received signals, going as low as 1-bit per signal sample.

### Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model.

### Structured Illumination and Variable Density Sampling for Compressive Fourier Transform Interferometry

Abstract: Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality for many applications demanding high spectral resolution, e.g., in fluorescence microscopy. However, the effective resolution of FTI is limited by the durability (or photobleaching) of biological elements when exposed to illuminating light.

### Time for dithering! Quantized random embeddings with RIP random matrices

(invited by H. Tyagi and M. Cucuringu) Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.

### Taking the edge off quantization: projected back projection in dithered compressive sensing

Joint work with C. Xu and V. Schellekens.