Deep Learning Classification Algorithm for Denoising Video Sequences
Nikolay Gaidarov, Nicole Christoff · 2024
This paper reviews methods and algorithms for processing noisy video sequences and presents the development of a novel algorithm designed to classify and denoise video sequences affected by specific types of noise, including Gaussian and Salt-and-Pepper noise. The proposed algorithm first classifies the type of noise present in the video sequence and then applies the appropriate filtration technique based on the classification results. The effectiveness of the algorithm is tested on at least three RGB video sequences, each with a frame rate of 30 frames per second and a minimum duration of 10 seconds. Experimental results demonstrate the algorithm's capability to accurately classify noise types and effectively denoise video sequences, highlighting its potential for improving video quality in various applications.