Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light

Abstract

Haze removal is an ill-posed problem that has attracted much scientific interest due to its various practical applications. Existing methods are usually founded upon various priors; consequently, they demonstrate poor performance in circumstances in which the priors do not hold. By examining hazy and haze-free images, we determined that haze density is highly correlated with image features such as contrast energy, entropy, and sharpness. Then, we proposed an iterative algorithm to accurately estimate the extinction coefficient of the transmission medium via direct optimization of the objective function taking into account all of the features. Furthermore, to address the heterogeneity of the lightness, we devised adaptive atmospheric light to replace the homogeneous light generally used in haze removal. A comparative evaluation against other state-of-the-art approaches demonstrated the superiority of the proposed method. The source code and data sets used in this paper are made publicly available to facilitate further research.

Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems

“…Therefore, it is better to exclude this algorithm from our list of benchmarking methods. However, we will demonstrate later in Section 4.3 that the proposed method is comparable to that of Ngo et al [ 21 ] using their reported results.…”

“…This section presents a comparative evaluation of the proposed algorithm and four benchmarking methods, including those proposed by He et al [ 7 ], Zhu et al [ 15 ], Kim et al [ 15 ], and Galdran [ 36 ]. As mentioned in Section 1 , although the recent method proposed by Ngo et al [ 21 ] is quite efficient in image quality, its costly computations require considerable effort for future research. Therefore, it is better to exclude this algorithm from our list of benchmarking methods.…”

“…To conclude this section, a brief comparison of the proposed method with Ngo et al [ 21 ] is in order. According to their reported results, the pairs of {SSIM, TMQI} scores were , , and on FRIDA2, O-HAZE, and I-HAZE datasets, respectively.…”

“…Tang et al [ 20 ] utilized another machine learning technique called random forest regression to calculate the extinction coefficients from a set of multi-scale image features including DCP, local maximum contrast, hue disparity, and local maximum saturation. Ngo et al [ 21 ] exploited the simplex-based Nelder-Mead optimization to find the optimal transmission map. They additionally devised an adaptive atmospheric light to account for the heterogeneity of lightness.…”

“…They additionally devised an adaptive atmospheric light to account for the heterogeneity of lightness. Although the dehazing power of the methods proposed by Tang et al [ 20 ] and Ngo et al [ 21 ] is quite impressive, several practical difficulties resulted owing to their highly expensive computations. Recently, deep learning techniques, notably convolutional neural networks (CNNs), have been used to learn the unknown variables of the Koschmieder model from the collected data.…”