Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.072893
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Boruta-LSTMAE: Feature-Enhanced Depth Image Denoising for 3D Recognition

Fawad Salam Khan1,*, Noman Hasany2, Muzammil Ahmad Khan3, Shayan Abbas4, Sajjad Ahmed5, Muhammad Zorain6, Wai Yie Leong7,*, Susama Bagchi8, Sanjoy Kumar Debnath8
1 Campus Cité Scientifique, Université de Lille, Villeneuve-d’Ascq, Lille, France
2 College of Computer Science and Information Systems, Institute of Business Management, Karachi, Pakistan
3 Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan
4 Incflex Ltd., London, UK
5 Department of Computer Science, The University of Larkano, Sindh, Pakistan
6 Department of Creative Technologies, Faculty of Computing & AI, Air University, Islamabad, Pakistan
7 Faculty of Engineering and Quantity Surveying, INTI International University, Nilai, Malaysia
8 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
* Corresponding Author: Fawad Salam Khan. Email: email; Wai Yie Leong. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.072893

Received 05 September 2025; Accepted 26 December 2025; Published online 14 January 2026

Abstract

The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors, due to the limited capabilities of sensors, which also produce poor computer vision results. The common image denoising techniques tend to remove significant image details and also remove noise, provided they are based on space and frequency filtering. The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder (LSTMAE). The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy. An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust, noise-resistant representations. The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image. Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90, which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models. Moreover, the feature selection step will decrease the input dimensionality by 40%, resulting in a 37.5% reduction in training time and a real-time inference rate of 200 FPS. Boruta-LSTMAE framework, therefore, offers a highly efficient and scalable system for depth image denoising, with a high potential to be applied to close-range 3D systems, such as robotic manipulation and gesture-based interfaces.

Keywords

Boruta; LSTM; autoencoder; feature fusion; denoising; 3D object recognition; depth images

  • 232

    View

  • 33

    Download

  • 1

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link