Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator

Abstract

Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.

A Symmetric Multiprocessor System-on-a-Chip-Based Solution for Real-Time Image Dehazing

“…This implies that the haze distribution depends on scene depth, allowing the dehazing algorithm to handle various types of haze, from mild to dense. In a prior study [38], we introduced a framework for generating a piece-wise linear weight using the haziness degree estimator [37]. This weight is combined with the scene depth in a multiplicative manner to address different scenarios:…”

“…In [38], evaluation results indicated that dehazing performance for densely hazy images was unimpressive, suggesting that greater dehazing power might improve results. Consequently, in this study, we have modified the original weighting scheme by allowing the weight (ω) to extend beyond the range [0, 1], up to a predefined value of W, as illustrated in Figure 2b.…”

“…Figure 2 illustrates a comparison between the original weight presented in [38] and our proposed weight to highlight their differences. It features a densely hazy image from the IVC dataset [39] and showcases two dehazing results obtained using the two weights, respectively.…”

Singe Image Dehazing With Unsharp Masking and Color Gamut Expansion

“…In the proposed method, the enhanced equidistribution [31] supersedes SUD to improve the statistical reliability of the synthetic dataset. Additionally, the mini-batch gradient ascent with an adaptive learning rate [34] replaces SGA to reduce the convergence time.…”

“…To describe chrominance interpolation, we reused ch ij , cb ij , and cr ij to denote a component of Ch f , Cb t , and Cr t in the i-th row and j-th column, respectively. Given Ch f = {ch ij ∈ R}, Cb t and Cr t are obtained by interlacing Ch f with zeros, as (34) and (35) show.…”