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 is consisted of an encoder and a decoder, where 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 auto encoder.

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

“…Moreover, some latest state-of-the-art medical image denoising techniques include the use of a bio-inspired optimization-based filtering system implemented by CNN [32], where Gaussian and spatial weights influence the deliberation of medical images. The issue of robustness in medical image denoising and classification is addressed by [33,34], which successfully performs the task of image classification and denoising by amalgamating various CNN frameworks, naturally implanted auto-encoder, and high-level feature invariance at two sets of medical image tasks.…”

Low-Dose COVID-19 CT Image Denoising Using Batch Normalization and Convolution Neural Network

“…This was addressed in a study that aimed to classify, identify, and analyze the CT scans of COVID-19 patients via implementing deep learning to develop an ultra-low-dose CT examination. While the results show great efficacy in classifying lesions into GGO, crazy paving, CS, nodular infiltrates (NI), broncho-vascular thickening (BVT), and pleural effusion (PE), a detailed literature review and comprehensive comparative analysis could edify the significance of the proposed methodology [33][34][35][36][37][38][39][40].…”

High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder

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