Application of wavelet transform-based wiener filtering method to reduce additive noise in apple image

A wavelet transform-based Wiener filtering method was put forward. The method was applied in reducing additive noise in apple images, as a result, PSNR was 184.94 (visual effect was clear), better than other methods such as neighborhood average (PSNR was 174.15), median filter (PSNR was 182.42), wavelet thresholding de-noise (PSNR was 171.59) and Wiener filter (PSNR was 173.65), and much better than mathematical morphology (PSNR was 150.46, with much noise in visual effect). The experimental results showed that wavelet transform-based Wiener filtering method applied in reducing additive noise in apple image has the advantages of high signal-to-noise, better visual effect than conventional de-noise methods, wavelet thresholding de-noise and Wiener filter de-noise. So wavelet transform-based Wiener filtering method applied to reduce additive noise in apple image is effective and practicable.