From the course: Deep Learning: Image Recognition

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Challenge: Dealing with noise in images

Challenge: Dealing with noise in images - Python Tutorial

From the course: Deep Learning: Image Recognition

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Challenge: Dealing with noise in images

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(bright upbeat music) - While we talked about challenges in Image Recognition, such as different lighting conditions, occlusions, variations in scale, dealing with class imbalances, inter-class similarity, but there's one more left we haven't talked about, and that is dealing with noise. So, Introduction to Noise. So, let's see what noise is, first of all. Noise in images can be thought of as random variations in brightness or color information. Noise can come from various resources, like sensor limitations, environmental conditions, or even during the transmission of images we may introduce noise. In our context, noise appears as random specs or variations that obscure the true details of the image. Well, why is this a problem for us for deep learning? For our models, noise can be particularly problematic. Models trained on clean, noise-free data may actually struggle to recognize patterns or objects when noise is introduced. This is because the noise can distort important features…

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