Frame Prediction- Noise Removal using Denoising Autoencoders
Manju D2, Seetha M, Sammulal P2

1Mrs. Manju D*, Assistant Professor, Dept.of CSE, GNITS, Hyderabad, India.
2Dr. Seetha M, Professor & HOD, Dept. of CSE, GNITS, Hyderabad, India.
3Dr. Sammulal P, Professor, Dept.of CSE, JNTUH CEJ, Hyderabad, India

Manuscript received on November 12, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 5296-5299 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7135129219/2019©BEIESP | DOI: 10.35940/ijitee.B7135.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: In the current times, tasks like Object Detection, Object tracking, Gesture prediction, Video prediction in computer vision are being solved effectively with models of deep learning . Video frame prediction involves predicting the next few frames of a video given the previous frame or frames as input. Currently, the challenge in video frame prediction is that the predicted future frames are blurry. This paper focuses on the removal of noise from the predicted image using Denoising Autoencoders, solve the above-addressed issue. The proposed work, trains LSTM model which generates future frames by giving a sequence of input frames. The predicted output is given as an input to the Denoising Autoencoders which tries to remove the blurry predictions. Our approach is implemented on Moving MNIST Dataset. The result of our proposed method improved accuracy and is compared with the accuracy of Denoising Autoencoders, LSTM, and LSTM along with Denoising Autoencoders. 
Keywords: Denoising Autoencoders, Long short-Term memory (LSTM), Moving MNIST Dataset, Prediction.
Scope of the Article: Health Monitoring and Life Prediction of Structures