Feature-Fusion-Based Haze Recognition in Endoscopic Images

Haze generated during endoscopic surgeries significantly obstructs the surgeon’s field of view, leading to inaccurate clinical judgments and elevated surgical risks. Identifying whether endoscopic images contain haze is essential for dehazing. However, existing haze image classification approaches usually concentrate on natural images, showing inferior performance when applied to endoscopic images. To address this issue, an effective haze recognition method specifically designed for endoscopic images is proposed. This paper innovatively employs three kinds of features (i.e., color, edge, and dark channel), which are selected based on the unique characteristics of endoscopic haze images. These features are then fused and inputted into a Support Vector Machine (SVM) classifier. Evaluated on clinical endoscopic images, our method demonstrates superior performance: (Accuracy: 98.67%, Precision: 98.03%, and Recall: 99.33%), outperforming existing methods. The proposed method is expected to enhance the performance of future dehazing algorithms in endoscopic images, potentially improving surgical accuracy and reducing surgical risks.