Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene feature

Abstract

Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a "sky area", and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area ("non-sky"). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel, Fattal, Ren, and Berman based on the criteria of no-reference quality assessment (NRQA), blind/referenceless image spatial quality evaluator (BRISQUE), blind anistropic quality index (AQI), and e.

Visibility Restoration: A Systematic Review and Meta-Analysis

“…Furthermore, those involving airlight estimation also lacked generality, because they required the presence of sky areas to function correctly. Recently, Qu and Zou [19] and Liang et al [20] attempted to overcome this problem, but the results were unimpressive.…”

“…Physics-based contrast enhancement [2] Block-overlapped histogram equalization [10] Contrast stretch concept [11] Polarimetric image acquisition [13] Polarized airlight assumption [12,15,16] Polarized airlight and polarized object assumption [14] FPGA implementation [17] Non-sky estimation of airlight [18,19] Application of DCP in underwater image enhancement [21] Application of DCP in medical image processing [22][23][24] Guided image filtering [26] and its variants [27][28][29] Pixel-wise dark channel and bilateral filtering [30] Extreme channel, morphological operations, and bilateral filtering [31] Morphological operations [32] Variation of dark channel [33] Filtering approach [34][35][36] Optimized DCP [37] Improvement on refinement FPGA implementation favored by single-scale fusion [39] Transmittance fusion [41] RGB-NIR fusion [43,44] Multiscale fusion based on Laplacian representation [38,40,42] Generalized logarithmic image processing model [46] Biologically inspired retina model [47] Hybrid model combining filtering approach and image fusion [48] Color ellipsoid prior [49] Patch similarity prior [50] Improvement on prior Original DCP…”

An Iterative Image Dehazing Method With Polarization

“…Figs. [14][15][16][17][18][19][20] show some examples of comparison between popular methods and our proposed method. In this paper, the parameter λ in ( 21) is set as 1 and the parameter β in ( 23) is set as 2.…”

“…They found that visual improvement was achieved especially in extreme weather. With a similar approach, Qu et al [14] used the brightest pixels of both the intensity image and the polarization difference to estimate the airlight. And DPA is calculated based on the dependency between DPA and airlight.…”

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

“…Image dehazing is very critical to obtain clear images in hazy weather [1] and scattering underwater environments [2]. Many techniques have been proposed to enhance the quality of hazy images through image processing [3], infrared-visible image fusion [4] and polarimetric imaging [5][6][7][8].…”