Blind spectrum reconstruction algorithm withL₀-sparse representation

Abstract

Raman spectrum often suffers from band overlap and Poisson noise. This paper presents a new blind Poissonian Raman spectrum reconstruction method, which incorporates the L0-sparse prior together with the total variation constraint into the maximum a posteriori framework. Furthermore, the greedy analysis pursuit algorithm is adopted to solve the L0-based minimization problem. Simulated and real spectrum experimental results show that the proposed method can effectively preserve spectral structure and suppress noise. The reconstructed Raman spectra are easily used for interpreting unknown chemical mixtures.

Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation

“…Facial expression is one of the most powerful, natural, and universal signals for human beings to convey their emotional states and intentions regardless of national borders, race, and gender [1,2], and there were multitudinous related applications such as the health management [3], aided driving [4,5], and others [6][7][8][9]. In the earlier researches on facial expressions of emotion, Ekman and Friesen argued that human beings perceive certain basic emotions in the same way regardless of their cultural background, and they defined the typical facial expressions into 6 categories: anger, disgust, fear, happiness, sadness, and surprise [10,11].…”

Blind infrared spectroscopic data restoration with the similarity of multi-scales regularization

“…, and noise amplification ratio (NAR) [24,25]. Among these indexes, the CC and WCC require the existence of a reference spectrum.…”

Blind Spectral Signal Deconvolution with Sparsity Regularization: An Iteratively Reweighted Least-Squares Solution

“…Spectral signals have become the preferred tools for characterization and analysis of materials [2,12,13,18,23,25], but they often are affected by band overlap and random noise, which limit their use. The spectral signal as measured by a dispersion spectrophotometer can be mathematically modeled as a latent spectrum convoluted with the instrument response function (IRF), i.e.,…”