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Abstract : first_pagesettingsOrder Article Reprints Open AccessCommunication A Deep Learning Framework to Remove the Off-Focused Voxels from the 3D Photons Starved Depth Images by Suchit Patel 1,2,†ORCID,Vineela Chandra Dodda 1,†ORCID,John T. Sheridan 3 andInbarasan Muniraj 1,4,*ORCID 1 Department of Electronics and Communication Engineering, School of Engineering and Science, SRM University AP, Amaravathi 522240, India 2 Department of Computer Engineering, Poornima College of Engineering, Jaipur 302022, India 3 School of Electrical and Electronic Engineering, College of Architecture and Engineering, University College Dublin, D4 Belfield, Ireland 4 LiFE Laboratory, Department of Electronics and Communication Engineering, Alliance College of Engineering and Design, Alliance University, Bengaluru 562106, India * Author to whom correspondence should be addressed. † These authors contributed equally to this work. Photonics 2023, 10(5), 583; https://doi.org/10.3390/photonics10050583 Received: 15 March 2023 / Revised: 10 May 2023 / Accepted: 15 May 2023 / Published: 17 May 2023 (This article belongs to the Special Issue Research in Computational Optics) Download Browse Figures Review Reports Versions Notes Abstract Photons Counted Integral Imaging (PCII) reconstructs 3D scenes with both focused and off-focused voxels. The off-focused portions do not contain or convey any visually valuable information and are therefore redundant. In this work, for the first time, we developed a six-ensembled Deep Neural Network (DNN) to identify and remove the off-focused voxels from both the conventional computational integral imaging and PCII techniques. As a preprocessing step, we used the standard Otsu thresholding technique to remove the obvious and unwanted background. We then used the preprocessed data to train the proposed six ensembled DNNs. The results demonstrate that the proposed methodology can efficiently discard the off-focused points and reconstruct a focused-only 3D scene with an accuracy of 98.57%.