High-throughput deconvolution-resolved computational spectromete...A novel high-throughput spectrometer with a wide-slit is presented. In conventional spectrometers, the slit limited the light throughput. Here, the slit is replaced with a much wider one (200 μm) to increase throughput. A beam splitter is utilized to ...
Improving spatial resolution in fiber Raman distributed temperat...The deconvolution algorithm is adopted on the fiber Raman distributed temperature sensor (FRDTS) to improve the spatial ... is enhanced by four times using the frequency-domain deconvolution algorithm with high temperature accuracy. In experiment, a spatial ...
Restoration of solar and star images with phase diversity-based ... ... -based telescope are often degraded by atmospheric turbulence and the aberration of the optical system. Phase diversity-based blind deconvolution is an effective post-processing method that can be used to overcome the turbulence-induced degradation. The ...
Gauss-Newton based kurtosis blind deconvolution of spectroscopic...The spectroscopic data recorded by dispersion spectrophotometer are usually degraded by the response function of the instrument. To improve the resolving power, double or triple cascade spectrophotometer and narrow slits have been employed, but the total ...
Constrained high-order statistical blind deconvolution of spectr...A constrained high-order statistical algorithm is proposed to blindly deconvolute the measured spectral data and estimate the response function of the instruments simultaneously. In this algorithm, no prior-knowledge is necessary except a proper length ...
Blur kernel estimation using sparse representation and cross-sca...Blind image deconvolution, i.e., estimating both the latent image and the blur kernel from the only observed blurry image, is a severely ill-posed inverse problem. In this paper, we propose a blur kernel estimation method for blind motion deblurring using ...
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