![]() These degradations have been carefully engineered to resemble the type of artifacts commonly found in noisy raw and lossy compressed digital SD video. During the learning phase, the CNN is presented with tens of thousands of image pairs of artificially degraded and perfect image patches. As few assumptions are made, the model is designed for real-world scenarios where conditions can change rapidly, producing a different noise distribution for every shot. ![]() Our deep convolutional neural network (CNN) architecture uses a combination of spatial and temporal filtering, learning how to spatially denoise frames and then optimally combine the effects of motion, brightness variations, and temporal imperfections to generate the denoised output. In a nutshell, this AI filter can reduce: This solution can reduce noise in digital video in an automated fashion, as opposed to going through the time-consuming task of hand-tuning multiple parameters and/or noise profiles using off-the-shelf video editing packages and plugins. Pixop Denoiser is our solution to enhancing the perceived visual quality of noisy video and is the ideal preprocessing step before applying our Pixop Deep Restoration filter.
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