A Symmetric Multiprocessor System-on-a-Chip-Based Solution for Real-Time Image Dehazing

Abstract

The acquisition of digital images is susceptible to haze, and images captured under such adverse conditions may impact high-level applications designed for clean input data. Image dehazing emerges as a practical solution to this problem, as it can be employed to pre-process images immediately after acquisition. This paper presents a concise review of impactful algorithms, including those based on deep learning models, to identify the existing gap in real-time processing capabilities. Subsequently, a real-time dehazing system on a multiprocessor system-on-a-chip (MPSoC) platform is introduced to bridge this gap. The proposed system balances the trade-off between dehazing performance and computational complexity; hence, the name “Symmetric” is coined. Additionally, the entire system is implemented in programmable logic and wrapped by an interface circuit supporting double-buffering, rendering it highly suitable for seamless integration into existing camera systems. Implementation results on a Zynq UltraScale+ MPSoC ZCU106 Evaluation Kit demonstrate a maximum operating frequency of 356.51 MHz, equivalent to a maximum processing speed of 40.27 frames per second for DCI 4K resolution.

Keywords

• image dehazing
• deep learning
• real-time processing
• MPSoC