In the light of the regularization method introduced in [12], two families of (C^{4} -smooth funC^{4} -smoothsilon}(cdot)) and (m_{epsilon}(cdot)) are employed to approximate (M(cdot)) and (m(cdot)), respectively, where ϵ is a small positive number.
In this work, in order to achieve stable estimation from a limited number of inputs, we introduce a physical constraint and ℓ 1-norm regularization into a light field reconstruction algorithm formulated as a convex optimization problem.
This model is tailored to the specific characteristics of time-lapse light microscopy datasets, as it provides the regularization needed to solve optical flow robustly for these types of volumes.
A proper regularization approach is then used to estimate the light source position, scene structure, and blur parameter.
In this paper, we have presented a novel 4-D light field reconstruction technique utilizing a physical constraint and a regularization.
The dynamic light scattering data were produced by the program performing a regularization fit using the Dynals algorithm on the resultant autocorrelation functions.
The size distribution of scattering particles was reconstructed from the scattered light correlation function using PrecisionDeconvolve software (Precision Detectors) based on the regularization method by Tikhonov and Arsenin (Tikhonov and Arsenin, 1977).
In this subsection, the effect of regularization constraints is analyzed using a simulated low-light image with σ=5. Figure 3 shows the results of proposed method using various different regularization parameters to analyze the effect of each regularization constraint.
In light of the uncertainty inherently presents in experimental data, a combination of Tikhonov regularization and optimal design of experiments (or data selection) is proposed to remedy uncertainty amplification from the data to the solution and circumvent unreliable screening.
"Regularization does not mean rewarding those who break the law.
