AMC-SSDA
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This is an Adaptive Multi Column Stacked Sparse Denoising Autoencoder technique to denoise images. In this project, we are trying to improve the result of denoised images using other deep learning techniques like GAN and modified autoencoder. ...learn more
Project status: Concept
Intel Technologies
DevCloud
Overview / Usage
This project is regarding to denoising of images. Firstly, we implemented AMC-SSDA techniques and then tried to implement other techniques of deep learning like GAN and other modified autoencoder to improve the prior denoised results. Nowadays, denoising an image is an important task before applying any type of AI based approach and most of the images become noisy at the time of capturing. So, this technique helps to obtain better accuracy on other AI based tasks like object detection by extracting better and more features from the image.
Methodology / Approach
Methodology of AMC-SSDA:
In this technique, firstly we have trained individual SSDA for each noise and then combined those results. After that, results from each SSDA was combined with the help of quadratic programming during training.The role of quadratic programming was to give weightage to each output according to the target. On the other hand, we do not have any target during the testing time. Therefore, RBF kernel was trained in which feature vector of each SSDA is taken as input and the weights generated by SSDA's are targets. As a result, during the testing time it will follow the mentioned chronology where test image will pass through each SSDA and store those SSDA's result along with feature vectors. After that, those feature vectors will be input of the RBF and will output the predicted weights according to their vectors. Finally, weighted sum of the outputs of each SSDA will be the final result. A modified AMC-SSDA technique is proposed in this project.