基於機器學習之車牌影像解析度增強技術

Learning-Based Super-Resolution for Car License Plate Images

林嘉文(Chia-Wen Lin) ； 許穎華(Ying-Hua Hsu)

《前瞻科技與管理》 2卷1期 (2012/05) Pp. 53-66

摘要

由一張低解析度的車牌影像去辨識出車牌號碼是一件困難的事。本研究提出一種基於基底分類與基於機器學習法的車牌超解析度影像重建方法。我們的方法先將車牌影像中的單一字元切割出並投影至正交區域保留投影（OLPP）空間中，再使用貝氏分類器找出所屬的字元類別，再利用所定義的成本函數修正由貝氏分類器的錯誤分類結果，最後再經由類別判定的步驟得到每個字元最有可能的分類。根據輸入字符分類的結果，我們可以選擇最合適的基底集合進行高解析度字符影像的重建。最後將各個字元重建的結果合成為一張高解析度的車牌影像。

關鍵字

超解析度 ； 貝氏分類器 ； 影像重建 ； 機器學習 ； 正交區域保留投影

並列摘要

In this paper, we propose a method based on classified bases and example-based super-resolution to reconstruct the high-resolution details of a single low-resolution car license-plate image. The proposed method first uses the Bayesian classifier to classify individual characters of a license plate in the Orthogonal Locality Preserving Projections (OLPP) subspace. The proposed cost function in the refinement step then corrects the initial classification result to further improve the classification accuracy. Based on the refined classification result, in the final class decision step, the best basis set is determined to reconstruct the high-resolution image. Our experimental results show that the proposed method effectively improves the visual quality of reconstructed high-resolution license plate images.

並列關鍵字

super-resolution ； Bayesian classifier ； image reconstruction ； machine learning ； Orthogonal Locality Preserving Projections (OLPP)

參考文獻

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