Narrow Results

An efficient detection of microcalcification based on edge enhancement using discrete wavelet transform (DWT) is presented in this paper. The proposed method is implemented by separating the wavelet coefficients into weak and strong edge coefficients for effective detection of microcalcification. Identification of strong and weak edge locations corresponding to microcalcification is obtained by allowing the input image through appropriate filters before wavelet decomposition. Before reconstructing the output image, the strong and weak edge coefficients are modified based on the energy of the coefficients. The reconstructed image exhibits a better enhancement with the fine detail components of microcalcification than the original mammogram image. Standard Mias mammogram database images and clinical mammograms are used for testing and comparing subjective and objective measures of the mammogram images. A comparative study is made with the existing state-of-the-art edge enhancement and contrast enhancement methods and results are encouraging. The edge emphasizing ability of the proposed method is highly proficient in detection of microcalcification from the mammogram.

Drawing on the rich theory of wavelets, a new method for vehicle license plate image denoising with edge preservation and enhancement is proposed in this paper, based on image multi-resolution decomposition by a redundant wavelet transform. As the experimental results show, a noticeable enhancement of number plate image quality(less noise without loss of important details) can be obtained by using this method. Furthermore, a good base for future segmentation or recognition is built.