在機器學習裡，經常會看到兩個詞，data fidelity term, regularization(prior) term.
例如，在image restoration中，我們需要最小化如下的object function(MAP, 最大後驗機率)，則：
data fidelity term:
regularization(prior) term:
那麼兩項的作用是什麼呢？
The fidelity term guarantees the solution accords with the degradation process,
翻譯：資料保真項保證結果符合降質過程
while the regularization term enforces desired property of the output.
翻譯：正則（先驗）項對輸出進行增強
Since IR is an ill-posed inverse problem, the prior which is also called regularization needs to be adopted to constraint the solution space
翻譯：因為image restoration是病态解問題，是以用正則（先驗）項限制解空間