Bölütleme Tabanlı Yeni Görüntü İyileştirme Yöntemi

Abstract

Histogram eşitleme yöntemi, görüntüde kontrastı ve parlaklığı ayarlamak için kullanılan temel görüntü işleme yöntemidir. Ancak histogram eşitleme, görüntülerde aşırı iyileşme, yapaylık, doygunluk ve ayrıntıların kaybolması gibi olumsuzluklar oluşturabilmektedir. Bu çalışmada görüntü bölütleme tabanlı yeni görüntü iyileştirme yöntemi önerilmiştir. Önerilen yöntemde görüntüdeki nesne bölgeleri aktif kontur tabanlı yöntemler ile bölütlenmiş ve bu bölgelerde histogram eşitleme uygulanmıştır. Daha sonra elde edilen iyileştirilmiş nesneler, giriş görüntüsündeki bölgesine eklenmiştir. Önerilen bu yöntem ile histogram eşitleme yönteminin görüntüler üzerinde oluşturduğu olumsuz etkiler önlenerek daha etkili iyileştirmeler sağlanmıştır. Ayrıca bölütleme yöntemi, histogram genişletme ve bi-histogram eşitleme yöntemleri ile birleştirilerek mevcut yöntemlerin başarısı da incelenmiştir. Görüntülerin entropi değeri, mutlak ortalama parlaklık hatası (AMBE) ve Tepe-Sinyal-Gürültü-Oranı (PSNR) metrikleri performans karşılaştırmasında kullanılmıştır. Elde edilen sonuçlar görsel ve sayısal olarak verilmiştir. Önerilen yöntem, mevcut histogram eşitleme tabanlı yöntemler ile karşılaştırılmış ve yöntemin başarısı ortaya çıkarılmıştır.

Keywords

• Görüntü iyileştirme
• Görüntü bölütleme
• Aktif kontur yöntemi
• Histogram eşitleme

A New Image Enhancement Method Based on Segmentation

Abstract

The histogram equalization method is the fundamental image processing method used to adjust the contrast and brightness in the image. However, histogram equalization can cause negative effects such as excessive enhancement, artifacts, saturation, and loss of details in images. In this paper, a segmentation-based new image enhancement method is proposed. With this proposed method, more effective enhancement is obtained by preventing the negative effects of the histogram equalization method on images. In the proposed method, the object regions in the image are segmented with active contour-based methods, and histogram equalization is applied to these regions. Enhanced objects obtained later are added to their region in the input image. With this proposed method, more effective enhancement is achieved by preventing the negative effects of the histogram equalization method on images. In addition, the success of the existing methods is examined by combining the segmentation method with histogram stretching and bi-histogram equalization methods. The entropy value of the images, the absolute average luminance error (AMBE), and the Peak-Signal-Noise-Ratio (PSNR) metrics are used in the performance comparison. The obtained results are presented both visually and numerically. The proposed method is compared with the histogram equalization-based methods, and the success of the proposed method is revealed.

Keywords

• Image enhancement
• Image segmentation
• Active contour method
• Histogram equalization.

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