Hello everybody, I join 43oh today! The method is to first design a filter based on wavelet transform and then attenuate the low-frequency part lx after performing wavelet transform of x layer on the picture, and then perform the inverse wavelet transform to achieve high-pass filtering, filtering out various kinds of pictures due to uneven illumination. The noise interference caused by factors highlights the license plate area. In traditional license plate positioning algorithms,https://www.sztigerwong.com, the images are first binarized. For the original grayscale image, there is a significant difference in brightness caused by uneven lighting. The brightness of the left half of the car is darker than the brightness of the right half due to building occlusion, and there is also a local lack of light in the right half. In the case of binarization of the average threshold value of the average gray level directly, it is easy to cause the license plate area to be unrecognizable because the threshold is selected too high. If the threshold is deliberately lowered in the program, it will introduce a lot of noise and lose the second The use of value. If local threshold binarization is used, on the one hand, the workload of calculating the threshold is increased, and the processing time is increased. At the same time, new boundary noise interference may be caused due to the division of the area. After using the high-pass filtering process based on wavelet transform, the picture effect is very ideal, which not only eliminates the influence caused by uneven lighting but also makes the license plate area more prominent, which greatly improves the accuracy of positioning and finding. However, the reconstructed signal during noise reduction preprocessing will lose the original time-domain features.