87 - Applications of Autoencoders - Denoising using custom images

Denoising or noise reduction in images is one of the many applications of autoencoders. This tutorial explains the process of building a denoising autoencoder in Python and testing it on your own images.

[eylulmood]

where did you get the noisy and clean images, please explain

[jameswood]

I am wondering how the accuracy is computed. For every input image, the output is almost certainly difference from the input. Is it computed by counting identical pixels? But they are floats.

[kaokuntai]

Could you provide the link for downloading your dataset?
Like Google Drive.

[computer-qzuz]

where did you get from the noise clean image, please explain

[ruhidave]

hi, can you please provide some details about autoencoder that you are using here.
like there are some types of autoencoder, so which one you are using here?

[unamattina]

how can I download the dataset you use in this video? other datasets on google I researched are about 6gb or similar. Could you please share the dataset on google drive or similar site?

[sriramsvk]

Thanks Sreeni for video series on autoencoders. With respect to Accuracy metrics, I think size of the image of also matters apart from sample size. Your thoughts on this pls.

[ellisiverdavid]

Hi, Sir Sreeni! :)

I’m just wondering—after we obtained the most important features from the bottleneck of our trained neural network, is it possible to apply the denoising capability of the autoencoder to a live feed video that is somewhat highly correlated to the training images?

Will this be better, or even recommended, instead of using traditional denoising filters of OpenCV for real-time videos?

I’d love to learn more from your expertise and advices as I explore this topic further. Thank you for the insightful explanation by the way! Subscribed! :)

[shayanmesdaghi]

Dear Dr. Sreenivas,
Thanks for your tutorial. I have two questions,
1. In your code, there are two paths, clean and noisy, but in GitHub, there is a series of images, do the images of noise and clean necessarily have to be the same? Or not?
2. I use echocardiography images. Unfortunately, whatever I do, the accuracy does not increase by 30%, but loss decreases below 2%. the number of images is 60, 000, and the size is 112. I change optimizers and loss functions. What is your suggestion?

[limchoonchen]

Hi, your video is so good and everything is explained in details in terms of coding and slide presentation. However, could you please make a video about the low-light image enhancement using CNN? It is extremely important for my study. Thank you

[adityanjsg]

Any CV problem, Sreeni has a video/solution!! Thanks.
Image Segmentation, Instance Segmentation and now Autoencoders.

[ruhidave]

Unable to allocate 524. MiB for an array with shape (1341, 320, 320, 1) and data type float32

can you please help me with this error?

[krishnakumars]

hello sir, really good to watch your videos. I have doubt . shall we remove skin hairs in the images using denoising autoencoder ?

[wiemrachman]

Hi Sir, where is "Save the Model" ?

[rubisharma]

Sir, in your github code of auto encoding single image, how is your model learning from single image and not large data set?An explaination will be friitful for my research paper.

[shibilit]

thanks for the video sir.. I tried to implement the same to denoise dicom CT images using Colab... but not getting result..can you suggest the changes to be made for CT dicom images

[mtahirrasheed]

The data used in this video is not available on your github link. from where we can download this dataset?

[mtahirrasheed]

can you please make a video on feeding the patches of high resolution images and then combining the patches to make the full image back. thanks a lot in advance.

[HaiderAli-pdwd]

Thanks for your outstanding explanation.I am getting this error would you give me some suggestions??

IndexError: index 2 is out of bounds for axis 0 with size 2

[LakshyaIIITD]

what if images are MNIST-back-image, how to denoise them ??