Source functions are obtained from $p-p$ and $d-\alpha$ correlation functions by applying the Richardson-Lucy (RL) deblurring to the Koonin-Pratt (KP) equation. To prevent the fitting of noise in the correlation function, total-variation (TV) regularization is employed, which has been effective in ordinary image restoration. TV alone cannot ensure the normalization of the source functions. To ensure the latter, we propose a maximum-entropy-regularized RL algorithm (MEM-RL). We outline the MEM-RL formalism and optimization strategy for the KP equation, demonstrating its effectiveness on both simulated and experimental data, including the $p-p$ and $d-\alpha$ correlation functions.

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