1. LAB MEETING
Speaker : Cheolsun Kim

2. Spectral reconstruction of compressive sensing spectroscopy using CNNs
Introduction
There has been a plenty of research interest to achieve compact spectrometers with a high spectral resolution, a wide working range, and a short measuring time.
Such spectrometer can be used in a broad range of fields such as remote sensing, forensics, and on-site detections.
Spectrometers exploiting signal processing methods can be candidate for these spectrometers.
Especially, compressive sensing (CS) framework makes it possible spectrometers to obtain the spectral resolution improvement with a compact size.

3. Spectral reconstruction of compressive sensing spectroscopy using CNNs
Introduction
To make effective signal recovery in CS spectroscopy, three components should be considered.
First, the spectrum should be a sparse signal or be sparsely represented in a certain basis.
Second, the sensing patterns should be designed to have a small mutual coherence.
Third, appropriated reconstruction techniques are needed.

4. Spectral reconstruction of compressive sensing spectroscopy using CNNs
Introduction
To date, there exist transform basis for sparsifying the spectrum such as a family of orthogonal Daubechies wavelet [], a Gaussian kernel matrix [], and a learned dictionary [].
In addition, numerous optical structures have been proposed to attain the small mutual coherence of the sensing patterns such as thin-film filters [], a liquid crystal phase retarder [], Fabry-Perot filters [], and photonic crystal slabs [].
As techniques for reconstructing the original signal, two kinds of basic techniques are iterative greedy techniques [], and convex relaxation techniques []. In CS spectroscopy, convex relaxation techniques have been used such as l1 norm minimization [], l2-l1 minimization [], and those with nonnegativity constraints [].
Combining the three considerations, CS spectrometers have been developed with stable performance in various light sources such as LEDs and monochromatic lights.

5. Spectral reconstruction of compressive sensing spectroscopy using CNNs
Introduction
To date, there exist transform basis for sparsifying the spectrum such as a family of orthogonal Daubechies wavelet [], a Gaussian kernel matrix [], and a learned dictionary [].
In addition, numerous optical structures have been proposed to attain the small mutual coherence of the sensing patterns such as thin-film filters [], a liquid crystal phase retarder [], Fabry-Perot filters [], and photonic crystal slabs [].
As techniques for reconstructing the original signal, two kinds of basic techniques are iterative greedy techniques [], and convex relaxation techniques []. In CS spectroscopy, convex relaxation techniques have been used such as l1 norm minimization [], l2-l1 minimization [], and those with nonnegativity constraints [].
Combining the three considerations, CS spectrometers have been developed with stable performance in various light sources such as LEDs and monochromatic lights.

6. Spectral reconstruction of compressive sensing spectroscopy using CNNs
Introduction
Due to the fact that not all signals are sparse in a fixed basis, a prior information of sensing patterns is essential, and the high computation cost is needed for reconstruction techniques. Recently, deep learning has been emerging as a promising alternative framework for reconstructing the original signal from the undersampled measurements.
Mousavi et al. [] was the first study of the image recovery from the undersampled measurements using the deep learning. Also, deep learning framework for inverse problems has been applied in X-ray computed tomography [], fiber bundle imaging [], and photoacoustic imaging []. Kim et al. [] reported the first attempt to use deep learning framework in CS spectroscopy.

7. Spectral reconstruction of compressive sensing spectroscopy using CNNs
Introduction
In this paper, we propose the modified LeNet, which is one of CNN architectures for spectral reconstruction. Unlike the previous work [], we train the CNN architecture to learn the differences between original spectra and inputs of the architecture. The reconstructed spectra are obtained by subtracting the estimated differences from the inputs of the architecture. We compare the proposed method with the previous work [] and the reconstruction technique [] which is conventionally used in compressive sensing spectroscopy. Numerical experiment results show fast reconstruction time, and better performances than existing reconstruction techniques.

8. Spectral reconstruction of compressive sensing spectroscopy using CNNs