An adaptive median filter using local texture information in images
Yuning Xie, Zhu Zhu, Xiaoguo Zhang, Qing Wang · 2014
After evaluating performances of the directional median (DM) filter and the adaptive switching median (ASWM) filter, we propose an adaptive median filter for restoring images by using local texture information in images. It contains two steps: 1) identifying noise pixels; 2) estimating the values of the noise pixels. Firstly, a double-layer window is adopted to improve the adaptive switching median filter, and the inner layer is used to detect noise pixels while the outer layer is used to obtain local texture information. According to the texture features, the detected noisy pixels are then restored by the center weighted median filter. Finally, experimental tests are done on evaluating the algorithm's time consumption, noise detecting rate, and restoration quality. The test results show our algorithm has satisfying performance on restoring high-corrupted images and is with lower time consumption compared to the existing approaches.