  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.
