Evaluation of underwater image enhancement algorithms based on Retinex and its implementation on embedded systems

• Published in: Neurocomputing (Amsterdam) 2022-07, Vol.494, p.148-159
• Main Authors: Aguirre-Castro, O.A., García-Guerrero, E.E., López-Bonilla, O.R., Tlelo-Cuautle, E., López-Mancilla, D., Cárdenas-Valdez, J.R., Olguín-Tiznado, J.E., Inzunza-González, E.
• Format: Article
• Language: English
• Subjects: Edge computing; Embedded system; Image enhancement; Retinex; Underwater image
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.04.074

The improvement of underwater imaging has advanced significantly due to its contribution to marine engineering and underwater exploration. This fact has been reflected in recent years with the proposal of numerous algorithms that improve the quality of underwater images. A benchmarking of three algorithms based on the Retinex models implemented on five high-performance embedded systems is presented herein. These algorithms are the Single Scale Retinex Model (SSR), Multi-Scale Retinex Model (MSR), and the Multi-Scale Retinex Model with Color Restoration (MSRCR). These algorithms perform the histogram equalization to distribute pixels, reduce the predominant color, perform color and contrast correction, and achieve an automatic white balance to improve illumination. This paper employs five edge devices such as Beagle Board, Odroid-XU4, Raspberry Pi 4, Jetson Nano, and Jetson TX2 to enhance underwater images and benchmark their performance. Four quality metrics without a reference image such as UIQM, UCIQUE, BRISQUE and Entropy are used to evaluate the quality of the enhanced underwater images. The MSRCR algorithm achieves the best quality results when it is implemented on Jetson TX2 embedded system. It has a difference of 0.46 s in the processing time of 147 × 196 pixels images concerning a high-performance personal computer (PC). Implementing these algorithms on embedded systems offers an excellent cost-benefit ratio versus a traditional PC, considering image quality metrics, precision, accuracy, energy consumption, price, lightweight, size, portability, and reliability. These findings hold great promise for unmanned and self-propelled underwater vehicles with artificial vision for exploration.