Edge-AI-Assisted Real-Time Dynamic Anaglyph 3D Image Synthesis Using Hybrid VLSI Architecture with Adaptive Depth Estimation
S. Usha, M. Kanthimathi · Journal of Circuits Systems and Computers · 2026
Expanding consumer interest in the visual experience, particularly in augmented reality (AR) and virtual reality (VR), has prompted the development of 3D imaging technologies in various forms. Conventional methods for anaglyph image production suffer from static scene assumptions, heavy computational burden and hardware. To alleviate these limitations, we present a novel hybrid architecture that combines Edge-AI to produce real-time, dynamic anaglyph 3D images employing hardware acceleration of the VLSI for adaptable depth estimation. Our approach utilizes an embedded CNN model tailored for resource-constrained edge devices. The depth is dynamically predicted for real-time input video frames without relying on multi-view or pre-captured stereo image pairs. Then the generated depth maps are dynamically fused with original RGB frames using the proposed four-operand wallace Tree Multiplier on an FPGA-based VLSI. This multiplier is based on an adaptive compressor logic that can adaptively switch between (7:2, 5:2, 4:2 compressors) based on real-time frame-to-frame motion estimation, which can save most of the duplicated computations and logic overhead. Experimental results evaluated on our in-house collected datasets that consist of various challenging dynamic scenes (i.e., fast moving, illumination variation and complex background) clearly show the effectiveness of the hybrid system. Preliminary results on the Xilinx UltraScale [Formula: see text] MPSoC show an average frame rate improvement of 271% in comparison to available three-operand VLSI designs. Moreover, extensive objective quality measurements using SSIM, PSNR and LPIPS metrics corroborate significant advancements in perceived depth accuracy and overall image quality. The great reduction of hardware and power consumption, together with dramatic enhancement of real-time performance and image quality, make the proposed method a smart, practical and novel approach for the next-generation AR/VR applications, mobile imaging and autonomous vision modules.