Open Access

ARTICLE

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

Fawad Salam Khan^1,*, Noman Hasany², Muzammil Ahmad Khan³, Shayan Abbas⁴, Sajjad Ahmed⁵, Muhammad Zorain⁶, Wai Yie Leong^7,*, 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 Authors: Fawad Salam Khan. Email: ; Wai Yie Leong. Email:

Computers, Materials & Continua 2026, 87(1), 91 https://doi.org/10.32604/cmc.2026.072893

Received 05 September 2025; Accepted 26 December 2025; Issue published 10 February 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.

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Keywords

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