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.
Highlights
There are two ways to shoot movies: film shooting and digital camera
We study the image denoising method based on partial differential equation and the image coloring method based on variational differentiation
The partial differential equation-based image denoising method can remove the noise while protecting the edges so that the detailed information of the image is not destroyed, and this method is of great interest to scholars
Summary
There are two ways to shoot movies: film shooting and digital camera. Digital cameras use CCD as the sensor, but a small percentage use CMOS, and the raw movie video is stored in digital format on tape, DVD, or hard disk. The most significant feature of spatiotemporal noise reduction filter is that it is itself a nonlinear three-dimensional signal processing process, which can reduce the spatial noise of each frame while taking advantage of the temporal information of the video sequence and effectively preserving the edge and texture information of the image [10]. The video processing based on spatiotemporal filtering will be targeted to repair the noise according to its specific characteristics, helping to solve some key problems in the field of digital TV and film picture quality improvement and effectively improve the performance of the signal system. It is shown experimentally that the proposed model is effective in preserving image edges and reducing color crossing for both structured and textured images
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