What is the data that the CNN is being trained on?
ess3ncez
As mentioned in the question above, machine learning-based demosaicking algorithms heavily rely on training data. The quality of the results is directly influenced by the quantity, quality, and diversity of the training dataset. If the training data is insufficient or not representative of real-world scenarios, the algorithm may not perform well. Moreover, machine learning models might overfit to the training data, which can lead to poor performance on unseen images. The model may fail to generalize to new or different scenarios, such as varying lighting conditions, textures, or noise levels.
ZiqiShi-HMD
What happens if we demosaic from pure Gaussian noise?
LeslieTrue
@ZiqiShi-HMD,
You might be interested in some generation models i.e. diffusion and GAN-style methods. These method are directly generating images from random noise. If you provide some condition such as text labels, it will generate what you want.
What is the data that the CNN is being trained on?
As mentioned in the question above, machine learning-based demosaicking algorithms heavily rely on training data. The quality of the results is directly influenced by the quantity, quality, and diversity of the training dataset. If the training data is insufficient or not representative of real-world scenarios, the algorithm may not perform well. Moreover, machine learning models might overfit to the training data, which can lead to poor performance on unseen images. The model may fail to generalize to new or different scenarios, such as varying lighting conditions, textures, or noise levels.
What happens if we demosaic from pure Gaussian noise?
@ZiqiShi-HMD, You might be interested in some generation models i.e. diffusion and GAN-style methods. These method are directly generating images from random noise. If you provide some condition such as text labels, it will generate what you want.