Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator

Abstract

Extreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition. To address this critical issue and to make object recognition accurate and invincible, we propose a hybrid digital–optical correlator specifically designed to perform under adverse weather conditions. This approach integrates the dark channel prior (DCP) with the fringe-adjusted joint transform correlator (FJTC), promising significant potential to enhance the robustness of the object recognition process under challenging environmental conditions. The proposed scheme presents a unique and alternative approach for object recognition under bad weather conditions. The incoming input scenes are processed with the DCP, enabling the FJTC to perform optical correlation on the refined images. The effectiveness of the proposed method is evaluated using several performance metrics like the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), correlation peak intensity (CPI), processing time, and recognition accuracy. To validate the performance of the proposed study, numerical simulation along with hybrid digital–optical demonstrations have been conducted.