Efficient Image Haze Removal Approach using Optimized Hardware Architecture Design using Parallel Processing

Abstract

The efficient hardware architecture for the image dehazing approach to remove haze in the photos is needed for many real-time applications that are automatic vehicle systems, CC cameras, multimedia applications. This work suggests the hardware architecture of dehazing which consists of two techniques: a low-complexity Simplified AirLight Estimating (SALE) approach and a Separate Transmission Map Estimation (STME) approach that can work on independently. First, SALE down-samples data, which essentially cuts down on data processing. Then, a Center-Point Compensation (CPC) method is created to make SALE AL predictions better. SALE doesn't use the sorting and filtering that is needed by image dehazing ways. By creating a preluded-AL estimate method, SALE improves the efficiency of hardware architecture data scheduling. STME makes transmission maps quickly and without needing AL data. When SMTE and SALE are processed at the same time, hardware performance goes up. When compared to other advanced studies, the PSNR, and SSIM values good for this work. For the Zynq7000 target FPGA, this suggested hardware design is made real. Based on the results of the experiment, this work uses less space and power.