Partial Differential Equations-Based Iterative Denoising Algorithm for Movie Images

Abstract

Film video noise can usually be defined as the error information visible on the video image, caused by the digital signal system. This distortion is inevitably present in the video obtained by various camera equipment. Noise reduction techniques are important preprocessing processes in many video processing applications, and its main goal is to reduce the noise contained in a video image while preserving as much of its edge and texture information as possible. In this paper, we describe in detail the principles of the space-time noise reduction filter, propose a 3D-filter algorithm for Gaussian noise, an improved 3D-filter algorithm based on the 3D-BDP (bloom-deep-split) filter for mixed noise, and a filter algorithm for luminance and color noise in low-brightness scenes. By dissecting the partial differential equation (PDE) denoising process, we establish a new iterative denoising algorithm. The partial differential equation method can be considered as the iterative denoising of the filter, and the first stage of the new algorithm uses wavelet-domain adaptive Wiener filter as the filtering base and achieves good results by adjusting the parameters. The proposed model in this paper is compared with the existing denoising model, and the analysis results show that the model proposed in this section can effectively remove multiplicative noise. The experimental report shows that the parameters set by the algorithm have some stability and can achieve good processing results for multiple images, which is an advantage over the partial differential equation method for denoising. The second stage of the algorithm uses the appropriate partial differential equation method to remove the pseudo-Gibbs in the first stage, which further improves the performance of the algorithm. After the image containing Gaussian noise is processed by the new algorithm, the pseudo-Gibbs effect, which often occurs in wavelet denoising, is eliminated, and the step effect, which occurs in partial differential equation denoising, is avoided; the details are better preserved, and the peak signal-to-noise ratio is improved, and a large number of experiments show that it is an effective denoising method.

Music Note Feature Recognition Method based on Hilbert Space Method Fused with Partial Differential Equations

“…The primary purpose of noise reduction is to lessen the amount of distracting background noise in a video while allowing the image's edges and textures to come through clearly. Pingli Sun et al (2021) provide a comprehensive explanation of the space-time noise reduction filter's workings, along with the development of a 3D-filter algorithm for Gaussian noise, an enhanced 3D-filter algorithm for mixed noise based on the 3D-BDP (bloom-deep-split) filter, and a filter algorithm for luminance and color noise in dimly lit scenes [14]. They build a novel iterative denoising algorithm by deconstructing the PDE denoising process.…”

Noise Removal From Movies by Using Hybrid Smooth Algorithms

“…Measuring higher enhance Image (frame) quality can be guaranteed when less noise detect after the process to the noisy image and gets an image as a result closer to the original image [10]. Here we used one of the most popular quality measurements available for noise removal [11].…”

“…And that the value of the correlation coefficient ranges from -1 to +1, meaning that -1≤ r ≤ +1, [14] . In our application, the correlation coefficient scale was used to compare the points of the two images produced after optimization and the original, and to note the extent of the correlation between them as mentioned in equation( 2) [10], the results of Ncc as shown in table (2) . The variation noise resources make the image enhancement process very challenge and so on to the movies.…”