One-dimensional image surface blur algorithm based on wavelet transform and bilateral filtering

Image noises are usually generated in the processes of collection, transmission, and storage of images, while some noises, named internal noises, come from the image itself. The noises decrease image visual effect and quality. Thus, it is very important to remove the noises from the images. In this paper, we propose a one-dimensional surface blur algorithm based on wavelet transform and bilateral filtering for image internal noise elimination and detail preservation. In our algorithm, we first transform the two-dimensional image into one-dimensional signal vectors by merging the pixels in each row and column of the image. Then, we decompose each of the vectors into two parts: the low-frequency and high-frequency components with a discrete wavelet transform. We further perform the bilateral filtering and a local variance-based thresholding method on the two components to smooth and denoise signals, respectively. Finally, we evaluate our algorithm’s performance in a group of face images. The experimental results show that our algorithm achieved better performance on image denoising and detail preservation than a set of traditional smoothing methods and the state-of-the-art. Our algorithm is a simple, effective, and easy-to-implement method, and it is suitable for image smoothing to improve the image’s visual effect and quality.