Haze removal based on multiple scattering model with superpixel algorithm

Wang, Rui; Li, Rui; Sun, Hongzan

doi:10.1016/j.sigpro.2016.02.003

Cited by 54 publications

(28 citation statements)

References 26 publications

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“…Tao, Feng, Xu, and Li (2012) used DCP for transmission estimation and applied APSF to refine the detail of the image. Recently, Wang, Li, and Sun (2016) proposed superpixel-based dehazing method with modified APSF. In their approach, authors extracted ambient light from selected superpixel and refined transmission map using Guided filter.…”

Section: Related Workmentioning

confidence: 99%

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

Cho

Kim

2018

Journal of Field Robotics

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This paper presents a real‐time and channel‐invariant visibility enhancement algorithm using a hybrid image enhancement approach. The proposed method is initially motivated by an underwater visual simultaneous localization and mapping (SLAM) failure in a turbid medium. The environments studied contain various particles and are dominated by a different image degradation model. Targeting image enhancement for degraded images but not being limited to it, the proposed method provides a highly effective solution for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method introduces a hybrid scheme of two image enhancement modules: a model‐based (extensive) enhancement and a model‐free (immediate) enhancement. The proposed method is validated by using simulated synthetic color images and real‐world color and grayscale underwater images. Real‐world validation is performed in various environments such as hazy indoor, smoky indoor, and underwater. Using the ground truth trajectory or clear images acquired from the same area but without turbidity, we evaluate the proposed visibility enhancement and camera registration improvement for a feature based (ORB‐SLAM2), a direct (LSD‐SLAM), and a visual underwater SLAM application.

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Section: Related Workmentioning

confidence: 99%

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

Cho

Kim

2018

Journal of Field Robotics

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“…The dark channel prior proposed by He et al [3] is a clearly defined prior based on the scene radiance, which attracts significant attention and has tremendous potential [15][16][17][18][19][20][21][22][23][24] despite receiving criticism for being invalid for uniformly bright objects [19,25]. However, previously reported methods considered the prior but undermined its merits owing to their employment with the local constant assumption, a Laplacian matting [26], or a guided filter [11] (discussed in Section 2.1).…”

Section: Introductionmentioning

confidence: 99%

“…Otherwise, a Laplacian matting [26] or guided filter [11], which is better at block effect removal, is employed [3]. Laplacian matting and guided filters are widely employed to regularize the initial transmission map [3,15,17]. These algorithms adopt the local linear assumption, i.e.,…”

Section: Introductionmentioning

confidence: 99%

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Transmission Estimation by Complex Assumption with Occlusion Handling

Liu

Yang

Zhu

et al. 2019

Mathematical Problems in Engineering

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Transmission estimation is a critical step in single-image dehazing. The estimate of each pixel describes the portion of the scene radiance that is degraded by hazing and finally reaches the image sensor. Transmission estimation is an underconstrained problem, and, thus, various assumptions, priors, and models are employed to make it solvable. However, most of the previous methods did not consider the different assumptions simultaneously, which, therefore, did not correctly reflect the previous assumptions in the final result. This paper focuses on this problem and proposes a method using an energy function that clearly defines the optimal transmission map and combines the assumptions from three aspects: fidelity, smoothness, and occlusion handling, simultaneously. Fidelity is measured by a novel principle derived from the dark channel prior, smoothness is described by the assumption of piecewise smoothening, and occlusion handling is achieved based on a new proposed feature. The transmissions are estimated by searching for the optimal solution of the function that can retain all the employed assumptions simultaneously. The proposed method is evaluated on the synthetic images of two datasets and various natural images. The results show that there is remarkable fidelity and smoothness in the transmission and that a good performance is exhibited for haze removal.

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“…GGD and asymmetric GGD (AGGD) were fitted to certain regular statistical properties of natural images to get the natural scene statistics (NSS) model in [33]. Wang et al [32] applied GGD to approximate an atmosphere point spread function (APSF) kernel to propose the efficient method to remove haze from a single image. Song et al [28] constructed a GGD based model to introduce more facial details into the initial image synthesis.…”

Section: Introductionmentioning

confidence: 99%