影像分割是一種將輸入圖片分解為更適合處理之區塊的演算法。因為此種特性，影像分割如今廣泛的被使用在影像處理的領域，然而當傳統的分割演算法被使用在影像增強或影像二值化的領域時，由於同一區塊中的像素同質性過高，容易發生過度/不足的增強效果或是在二值化時產生前景/背景的誤判。為了解決此類問題，我們提出了一種新的智慧型區塊分割法來增加影像增強或影像二值化的輸出品質。
影像增強是一種增加影像對比度，以期能提供更清晰之輸出的演算法。在使用智慧型區塊分割法於此種演算法時，由於可以採計到更多的區塊資訊做為參考，與一般的直方圖等化法比起來能提供更好的品質。此種方法的運作，首先是將輸入影像依據邊緣偵測資訊分割為許多大小不依的區塊。在分割結束後，每一個獨立的區塊將以Bilateral Bezier Curve方法進行轉換式的計算，而適用於全圖的轉換式將會由各別轉換式以加權的方式組成。
二值化做為一種把灰階影像轉換為二元影像的演算法，被廣泛的使用在影像辨識的領域。當把智慧型區塊分割演算法使用於影像二值化時，也同樣可以提供高品質的輸出。在使用上，首先輸入影像也將會依據像素的特性分割為許多大小不一的區塊。每一獨立的區塊接下來會依據區塊內像素的平均值及標準差做出分類：有較低平均值及較高標準差的區塊將以Otsu法做進一步的處理;較低平均值及標準差者可以被認為僅包含前景像素;其餘的區塊則被認為僅包含背景像素。
就結果來說，以智慧型區塊分割後個別處理每個區塊，因有效增加許多參考資訊從而提供更好的輸出品質。本篇論文的實驗部分也提供了很好的佐證：此類演算法可以提供更快速，更高品質以及更低的邊緣遺失率。

Image segmentation has been used widely in the area of image processing nowadays.Aiming at representing input image in a way which is easier to analysis, the precess makes the collection of local information for accurate calculation possible.However, when using this kind of technique on contrast enhancement or image binarization, traditional segmentation processes will cause the decrement of result quality due to ignore nearby pixels on edge pixels.In order to solve the problem, we propose intelligent block segmentation (IBS) based methods on both contrast enhancement and image binarization.
Contrast enhancement involves transforming the intensity from the original state to feature significant impact on display devices.When using IBS on this kind of algorithms, the proposed one can provide both high quality and low edge loss rate.Firstly, the image will be segmented into different sized sub-images according to pixel characteristics.The local transform function of each sub-image will then be calculated by Bilateral Bezier Curve histogram equalization method.Once all local functions are calculated, the global transform function can be estimated by combining all functions together with weighting.
Image binarization, however, is a process of converting gray-level into binary ones.The related algorithms can be classified as either high quality computation or high performance.After the input image is segmented, each sub-image will be classified as belonging to foreground or background ones.To sum up, by using IBS the result quality of both methods are increased.Experimental results also reveal that these methods can both providing high quality and effectiveness.

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