A New Agricultural Image De-noising Algorithm Based on Hybrid Wavelet Transform
The conventional de-noising methods cannot achieve an excellent result in de-nosing of agricultural images. To solve this problem, a new de-noising method based on Genetic Algorithm (GA) and Wavelet Transform was presented, which combines the advantage of Wavelet Transform de-noising and Wiener Filter together. At first, the image de-noised by Wavelet Transform was defined as the Hybrid Wavelet transforms initial Male parent, and that de-noised by Wiener filter was defined as Female parent; At second, the fitness value of each individual was evaluated by fitness function. Then, the hybrid and mutate operation was performed to extract the superior gene of the parents generation and to optimize the gene for generating the next generations child; At last, an offspring image which has both advantage of Male parent and Female parent was obtained after the finite order hereditary algebra. The algorithms performance is tested by the red jujube images and wheat images. Experimental results show that the image de-noising method has a higher PSNR (77.83 for red jujube and 79.89 for wheat) than conventional methods. The denoised images have the characters of lower noise and clearer edge.
Hybrid wavelet transform Wiener filter Genetic Algorithm Image de-noising PSNR
Fuzeng Yang Yanna Tian Liangliang Yang Huaibo Song Jinyi He
College of Mechanical & Electronic Engineering, Northwest A&F University Yangling, Shaanxi Province, College of Information Engineering, Northwest A&F University Yangling, Shaanxi Province, China College of Mechanical & Electronic Engineering,Northwest A&F University Yangling, Shaanxi Province, College of Mechanical & Electronic Engineering,Northwest A&F University Yangling, Shaanxi Province,
国际会议
成都
英文
328-332
2010-06-12（万方平台首次上网日期，不代表论文的发表时间）