# Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention

arXiv: https://arxiv.org/abs/1801.05141 · HTML: https://mlsanity.com/paper/1801.05141/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review](https://mlsanity.com/paper/2306.12834/) (2023)
- [Enhancing Speech Emotion Recognition Through Differentiable Architecture Search](https://mlsanity.com/paper/2305.14402/) (2023)
- [emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition](https://mlsanity.com/paper/2403.14083/) (2024)

## Abstract (arXiv)

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.