Abstract
Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.
Highlights
Images or videos taken outdoors usually suffer from an apparent loss of contrast and details owing to the inevitable adverse effects of bad weather conditions
We present a novel and simple image enhancement-based haze removal method capable of producing satisfactory results
A computationally efficient haze removal algorithm and its corresponding hardware implementation were presented in this paper
Summary
Images or videos taken outdoors usually suffer from an apparent loss of contrast and details owing to the inevitable adverse effects of bad weather conditions. Schechner et al [3] developed a method that took at least two images captured under different polarization to estimate the airlight and the scaled depth They reversed the hazy image formation process to restore the original haze-free scene radiance. Other studies [23,24,25,26] have attempted to improve performance by either increasing the receptive field via deeper networks or developing a more sophisticated loss function as a surrogate for the universally used mean squared error They all share the same problem of lacking a real training dataset comprising pairs of hazy and haze-free images. Since estimating two unknown variables in the Koschmieder model is somewhat computationally expensive, researchers have attempted to dehaze images employing image enhancement techniques.
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