Wavelet Beam proudly presents IRIS.AI, the new solution for video upscaling
It is important to apply video denoising before AI-based upscaling because noise in a video can make it more difficult for upscaling algorithms to produce high-quality results. Upscaling algorithms use AI techniques such as deep learning to add additional pixels to the video, and the quality of the results will depend on the specific algorithm used and the quality of the original video. When a video contains noise, the upscaling algorithm can misinterpret the noise as actual image details, leading to unwanted artifacts and decreased visual quality in the upscaled video. By removing the noise from the video before upscaling, it is possible to provide the upscaling algorithm with a cleaner, higher-quality input, which can result in better upscaling results and improved visual quality in the upscaled video. Additionally, upscaling algorithms can be computationally intensive, and processing a noisy video can significantly increase the computational requirements, leading to longer processing times and potentially decreased performance. By removing the noise from the video before upscaling, it is possible to reduce the computational requirements of the upscaling process and improve performance.