TY - JFULL AU - Jianbo Hu and Hongbao Wang PY - 2007/3/ TI - Adaptive Total Variation Based on Feature Scale T2 - International Journal of Electrical and Computer Engineering SP - 464 EP - 468 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/7379 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 2, 2007 N2 - The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to control the amount of de-noising in any region of image, according to relative information of local feature scale. Experimental results demonstrate the efficient of the proposed algorithm. Compared with standard Total Variation, our algorithm can better preserve smaller-scaled features and show better performance. ER -