I think that image processing helps a lot with noise for pictures taken at night, like the professor said. Smartphones have "night mode" features that help us take better pictures at night and remove noise from them. There are multiple things that go into these night mode features like longer exposure, ISO adjustments, and machine learning for recognizing common objects. I think that the most important thing, however, is that smartphones take multiple pictures at different exposures and combine them together to show a picture that removes noise as much as possible. This is why when we are taking pictures in night mode we have to wait for a few seconds for the camera to take multiple pictures.
KevinXu02
The night mode of smartphones allows a slight shift of the phone, but can still get a clear photo. This requires combining the information of multiple pictures and reduce the noise form slightly different views.
wilrothman
I feel like this relates to our anti-aliasing program in homework 1-- a problem which seemed impossible to solve but was indeed somehow possible using the Nyquist frequency. I wonder if we can resolve this noise issue in the same way. Is this a result of the firework being large and very far away? Is it a result of the smoke resulting from the firework? I feel like the image would be more aesthetic without the smoke. Also, is this Seoul, Korea?
jinweiwong
I have tried taking pictures of the stars in night mode on the iPhone and the stars usually turn out to be overblown and there would be a lot of noise. I guess this would be only natural since the stars are much brighter than the night sky so there would be a large difference in exposure.
yykkcc
I tried to take a zooming in picture of the full moon one night and I also noticed the noise around the moon, I held the phone for a while since at that time I thought that was because my hands were not stable. But now I realize that's even worse since that's in low light and long exposures.
yykkcc
Recently, I have seen some techniques that use machine learning models to reduce noise in images. We need to collect images containing noise and corresponding images with no or less noise as training data. This requires a lot of high-quality images. I think a very interesting way is to manually add noise to high-quality images. to generate large amounts of training data.
I think that image processing helps a lot with noise for pictures taken at night, like the professor said. Smartphones have "night mode" features that help us take better pictures at night and remove noise from them. There are multiple things that go into these night mode features like longer exposure, ISO adjustments, and machine learning for recognizing common objects. I think that the most important thing, however, is that smartphones take multiple pictures at different exposures and combine them together to show a picture that removes noise as much as possible. This is why when we are taking pictures in night mode we have to wait for a few seconds for the camera to take multiple pictures.
The night mode of smartphones allows a slight shift of the phone, but can still get a clear photo. This requires combining the information of multiple pictures and reduce the noise form slightly different views.
I feel like this relates to our anti-aliasing program in homework 1-- a problem which seemed impossible to solve but was indeed somehow possible using the Nyquist frequency. I wonder if we can resolve this noise issue in the same way. Is this a result of the firework being large and very far away? Is it a result of the smoke resulting from the firework? I feel like the image would be more aesthetic without the smoke. Also, is this Seoul, Korea?
I have tried taking pictures of the stars in night mode on the iPhone and the stars usually turn out to be overblown and there would be a lot of noise. I guess this would be only natural since the stars are much brighter than the night sky so there would be a large difference in exposure.
I tried to take a zooming in picture of the full moon one night and I also noticed the noise around the moon, I held the phone for a while since at that time I thought that was because my hands were not stable. But now I realize that's even worse since that's in low light and long exposures.
Recently, I have seen some techniques that use machine learning models to reduce noise in images. We need to collect images containing noise and corresponding images with no or less noise as training data. This requires a lot of high-quality images. I think a very interesting way is to manually add noise to high-quality images. to generate large amounts of training data.