Levenberg-Marquardt Algorithm Applied for Foggy Image Enhancement

Curila, Sorin; Curila, Mircea; Curila (Popescu), Diana; Grava, Cristian

doi:10.15388/23-INFOR533

Informatica

Levenberg-Marquardt Algorithm Applied for Foggy Image Enhancement

Volume 35, Issue 1 (2024), pp. 47–63

Sorin Curila

Mircea Curila Diana Curila (Popescu) Cristian Grava

https://doi.org/10.15388/23-INFOR533

Pub. online: 19 October 2023 Type: Research Article

Open Access

Received
1 September 2022

Accepted
1 October 2023

Published
19 October 2023

Abstract

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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Biographies

Curila Sorin

https://orcid.org/0000-0001-9865-9855

scurila@uoradea.ro

S. Curila graduated in 1994 with the applied electronics specialization from the Technical University of Cluj-Napoca, Romania, and received in 2000 his PhD in electrical engineering from the University of Oradea, Romania (Compression of 3D polygonal models). He holds a university professor position at the Department of Electronics and Telecommunications of University of Oradea, Romania. His research interests include: pattern recognition, signal and image processing, programming languages.

Curila Mircea

mcurila@uoradea.ro

M. Curila graduated in 1994 with the applied electronics specialization from the Technical University of Cluj-Napoca, Romania, and in 1995 with the mathematics specialization from the Babes-Bolyai University of Cluj-Napoca, Romania. He received in 2003 his PhD in electrical engineering from the University of Oradea, Romania. He holds a university professor position at the Department of Environmental Engineering of University of Oradea, Romania. His research interests include: virtual reality, signal and image processing, programming languages.

Curila (Popescu) Diana

curila_diana@yahoo.com

D. Curila (Popescu) graduated in 1995 with the mathematics specialization from the Babes-Bolyai University of Cluj-Napoca, Romania. She is a PhD student at the Faculty of Informatics and Science, University of Oradea, Romania. She is currently a mathematics teacher at Dacia School, Oradea, Romania.

Grava Cristian

https://orcid.org/0000-0002-6316-1368

cgrava@uoradea.ro

C. Grava is a senior member of IEEE. He received his PhD in electronics and telecommunications from the Politehnica University of Bucharest, Romania, and in images and systems, from the National Institute of Applied Sciences from Lyon, France, in 2003. He is a professor at the University Oradea, Romania, a member of the Department of Electronics and Telecommunications. His research interests include signal and image processing, modelling of complex systems and artificial intelligence.

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Keywords

least squares problem Levenberg-Marquardt foggy images image enhancement

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