Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention
This is an incremental improvement for image processing tasks, specifically in denoising and inpainting corrupted images.
The paper tackles image denoising and restoration by proposing a CNN-LSTM encoder-decoder with direct attention, which reconstructs highly corrupted images from MNIST with minimal error and outperforms a convolutional autoencoder in generating missing parts.
Image denoising is always a challenging task in the field of computer vision and image processing. In this paper, we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model consists of an encoder and a decoder, where the encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memory network. In the proposed model, the encoder reads an image and catches the abstraction of that image in a vector, where decoder takes that vector as well as the corrupted image to reconstruct a clean image. We have trained our model on MNIST handwritten digit database after making lower half of every image as black as well as adding noise top of that. After a massive destruction of the images where it is hard for a human to understand the content of those images, our model can retrieve that image with minimal error. Our proposed model has been compared with convolutional encoder-decoder, where our model has performed better at generating missing part of the images than convolutional autoencoder.