Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention
Abstract
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.
Cite
@article{arxiv.1801.05141,
title = {Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention},
author = {Kazi Nazmul Haque and Mohammad Abu Yousuf and Rajib Rana},
journal= {arXiv preprint arXiv:1801.05141},
year = {2018}
}