Real Time Architecture for Image De-Hazing

Abstract

Outdoor scenes are mostly affected by bad weather and observed objects in images suffer from poor visibility and contrast. Therefore de-hazing algorithms are utilized to eliminate haze and fog from images. This paper describes the real-time architecture for image de-hazing using dark channel prior method. In proposed architecture, hazy images are read in patch form and minimum intensity pixels are separated from every patch. These pixels of minimal intensity combine to form a dark vector which is used in atmospheric light computation. Atmospheric light value is used in assessment of transmission map. In order to avoid appearing of artifacts in recovered image, fast guided filter is utilized for transmission map refinement and then refined transmission map is used in recovery of scene. The hardware architecture is implemented on Xilinx VIVADO tool using VHDL language. A comparison of simulation results with MATLAB results validates the architecture of image de-hazing. Hardware architecture is synthesized using Xilinx Virtex board, Device XCVU190, Package FLGC2104, and Speed 1800. Results from synthesis show that architecture consumes 7130 slice registers and 48690 LUTs.