A Design of Image Dehazing Engine Using DTE and DAE Techniques

Abstract

An image dehazing technique can restore hazy images to clear ones. Under hazy conditions, it can be a preceding stage of advanced-driver assistance systems (ADAS) to keep driving images clear. ADAS pursues a larger image resolution with a higher frame rate to keep its reliability for driving safety. This trend drives the image dehazing to face a severe challenge of considerable throughput. In this study, a hardware-efficient image dehazing engine with low computational burdens is proposed. It comprises two core techniques: Depth-Refinement Transmission-Rate Estimation (DTE) and Distribute Airlight Estimation (DAE). DTE provides an accurate depth map to estimate transmission rate, where the depth of white-objects is carefully calibrated. Base on the depth information, DAE predicts the airlight with the average of distant illumination sources to compensate nonuniform haze concentration. DTE and DAE can reduce the number of line buffers by 33% at least, while keeping comparable image dehazing quality. This study is implemented using TSMC CMOS $0.18~\mu \text{m}$ technology. To construct a smooth data scheduling, it is organized into a six-stage pipelinging strategy with an interleaving access technique. The logic gate count is 14.4K, and the power consumption is 15.2mW@250MHz. With a two-level parallelism, its throughput can support 3, $840\times 2.160$ @60fps. The experiment results show this study presents the superior combined performance of energy efficiency, area efficiency and line buffer reduction.