Image dehazing by artificial multiple-exposure image fusion

Abstract

Bad weather conditions can reduce visibility on images acquired outdoors, decreasing their visual quality. The image processing task concerned with the mitigation of this effect is known as image dehazing. In this paper we present a new image dehazing technique that can remove the visual degradation due to haze without relying on the inversion of a physical model of haze formation, but respecting its main underlying assumptions. Hence, the proposed technique avoids the need of estimating depth in the scene, as well as costly depth map refinement processes. To achieve this goal, the original hazy image is first artificially under-exposed by means of a sequence of gamma-correction operations. The resulting set of multiplyexposed images is merged into a haze-free result through a multi-scale Laplacian blending scheme. A detailed experimental evaluation is presented in terms of both qualitative and quantitative analysis. The obtained results indicate that the fusion of artificially under-exposed images can effectively remove the effect of haze, even in challenging situations where other current image dehazing techniques fail to produce good-quality results. An implementation of the technique is open-sourced for reproducibility (https://github.com/agaldran/amef_dehazing).

Improved Color Attenuation Prior for Single-Image Haze Removal

“…A myriad of algorithms have been proposed to recover the visual quality of weather-degraded images to be as similar as possible to the original ones taken under clear weather conditions. Low-light image enhancement [1][2][3][4], rain removal [5][6][7][8], and image dehazing [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are cases in point. Haze removal ones, of all the algorithms developed for visibility restoration, have positive impacts on both photography and computer vision applications.…”

“…Generally, haze removal algorithms are categorized according to the number of input images they need. Due to sufficient input information, multi-image approaches [9][10][11][12] are superior to single-image ones [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] in terms of performance. Given the difficulty of collecting the external information, however, there is little interest from researchers.…”

“…Accordingly, recent efforts largely have led to single-image dehazing using two-dimensional images without any external knowledge. Such an algorithm, nonetheless, needs to impose priors on the recovered image, for example, enhanced contrast or less attenuated color [23]. The most popular prior is the Dark Channel Prior (DCP), proposed by He et al [13], which assumes that there exists a dark pixel in every color channel of the local patch around all pixels.…”

“…Nevertheless, the lack of real datasets of hazy images and their corresponding ground truth references imposes a limit on the performance of these methods. Yet a third way of approaching image dehazing problems is to consider dehazing as a traditional image enhancement task [23]. The goal is to produce a good quality dehazed image in lieu of estimating the haze distribution in this case.…”

Adaptive Multi-Scale Entropy Fusion De-hazing based on Fractional Order

“…Furthermore, recently developed algorithms using partial differential equations (PDEs) and gradient metric-based optimization were developed [52] [53] to avoid the usage of DCP-based stages and multiple (and manual adjustment of) parameters. Recently, an Artificial Multiple-Exposure Image Fusion (AMEF) de-hazing algorithm was proposed by Galdran [54], which represents the current state-of-the-art.…”