直方圖均化（Histogram Equalization, HEQ）是一種處理曝光不足或曝光過度的影像方法，它能參考目標的累積分佈函數（Cumulative Distribution Function, CDF）進行分佈的轉換，此方法直觀且不需繁複的細節，近年來有許多研究將此方法應用在不同的領域上以及解決不匹配的問題，例如強健語音辨識的噪音改善問題，或是自然語言處理的跨語料庫問題。

本論文針對HEQ進行幾項基本的實驗，並利用分類問題來評估該方法對於轉換問題是否有效。結果顯示，在資料量不足的情況所得到的粗糙CDF分佈曲線，HEQ反而會造成錯誤的映射而影響轉換的結果。HEQ直接轉換與間接轉換的作法對於線性轉換與非線性轉換皆有接近的效果，但間接轉換的作法在不同分類器的情況顯得不穩定。在資料量充足的前提下，HEQ和標準差倍數正規化法在線性轉換下達到相近的效果，但對於非線性轉換，HEQ則有比標準差倍數正規化法更好的效能。

Histogram equalization (HEQ) is a technology for improving the darkness and the brightness of the image by adjusting the gray levels based on the cumulative distribution function (CDF). In recent years, this method has been applied to different issues, including robust speech recognition for solving the mismatch between the noisy speech and the clean speech, and natural language processing for the cross-database problem.

This paper analyzed how histogram equalization may influence a simple classification problem by simulation. The results showed the rough curve of CDF caused by insufficient data would lead to the poor mapping between training and test data and degrade the performance. Direct and indirect operations of histogram equalization achieve similar performance for linear or non-linear transformation, while the performance of the indirect one is more sensitive to type of classifiers. With sufficient amount of training data, HEQ and mean-standard deviation weight (MSW) can achieve compatible performances for linear transformation, while HEQ appears superior for nonlinear transformation.

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