A new Artificial Vision Method for Bad Atmospheric Conditions

Curila, Mircea; Curila, Sorin; Novac, Ovidiu-Constantin; Novac, Mihaela Cornelia

doi:10.1007/978-90-481-3660-5_38

Cited by 1 publication

(3 citation statements)

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“…It is assumed that a collimated beam of light with a unitary cross-section traverses the dispersive environment of thickness dz (fog dispersion, see Fig. 3) (Curilă et al, 2020). The radiative transfer through fog is expressed by Schwarzschild's equation as follows:…”

Section: A Mathematical Model For Fogmentioning

confidence: 99%

“…Real-life atmospheric impressions are simulated by choosing the type of fog and adding an atmospheric veil by suitably establishing the local distances. Next, we present distance maps for LIMA-000011, ship and bridge images (Curilă et al, 2020;Tarel et al, 2010).…”

Section: A Mathematical Model For Fogmentioning

confidence: 99%

“…Section 3 describes the Levenberg-Marquardt algorithm we use to estimate the components of the vector of unknown parameters of a model describing the process under analysis. The mathematical model for the acquisition process of homogeneous fog time images is described in Section 4, a more complex approach is given in Curilă et al (2020). In Section 5 we propose an algorithm for improving fog degraded images (using simulated foggy images).…”

Section: Introductionmentioning

confidence: 99%

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Levenberg-Marquardt Algorithm Applied for Foggy Image Enhancement

Curila,

Curila (Popescu)

et al. 2023

Informatica

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In this paper, we introduce a novel Model Based Foggy Image Enhancement using Levenberg-Marquardt non-linear estimation (MBFIELM). It presents a solution for enhancing image quality that has been compromised by homogeneous fog. Given an observation set represented by a foggy image, it is desired to estimate an analytical function dependent on adjustable variables that best cross the data in order to approximate them. A cost function is used to measure how the estimated function fits the observation set. Here, we use the Levenberg-Marquardt algorithm, a combination of the Gradient descent and the Gauss-Newton method, to optimize the non-linear cost function. An inverse transformation will result in an enhanced image. Both visual assessments and quantitative assessments, the latter utilizing a quality defogged image measure introduced by Liu et al. (2020), are highlighted in the experimental results section. The efficacy of MBFIELM is substantiated by metrics comparable to those of recognized algorithms like Artificial Multiple Exposure Fusion (AMEF), DehazeNet (a trainable end-to-end system), and Dark Channel Prior (DCP). There exist instances where the performance indices of AMEF exceed those of our model, yet there are situations where MBFIELM asserts superiority, outperforming these standard-bearers in algorithmic efficacy.

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Section: A Mathematical Model For Fogmentioning

confidence: 99%

Section: A Mathematical Model For Fogmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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