This approach can handle various types of noise and adapt to different scenes dynamically, but it may require high computational resources and specific hardware such as GPUs or TPUs. Sparse representation can be applied to improve medical imaging quality for diagnosis and treatment.Ī third AI video denoising approach is based on deep learning, which uses neural networks to learn a mapping function from noisy videos to clean videos using large amounts of training data. ![]() This approach can remove additive noise effectively, but it may not be suitable for multiplicative noise or non-stationary noise. NLM can be applied to enhance low-light videos for security and surveillance purposes.Īnother AI video denoising approach is based on sparse representation, which decomposes and reconstructs the video using a dictionary of basis functions that can capture the energy of the signal with minimum coefficients. This approach can preserve fine details and textures well, but it may introduce blurring artifacts and fail to handle complex motions. ![]() One of the AI video denoising approaches is based on non-local means (NLM), which searches for similar regions in the whole video based on image blocks and averages them to remove Gaussian noise. In this paragraph, we will compare some of the main AI video-denoising approaches and discuss their applications in various domains. Different AI video denoising approaches have been proposed and developed over the years, each with its own strengths and weaknesses. Video denoising has various applications in domains such as entertainment, security, surveillance, medical imaging, and scientific research.
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