Underwater Visibility Enhancement IoT System in Extreme Environment

Imagery captured in extreme underwater environments often presents unique challenges, including blurred details, color distortion, and reduced contrast. These discrepancies largely emanate from the intricate interplay of light absorption and scattering within the aquatic medium. Predominant restoration techniques, rather simplistically, apply a static attenuation coefficient, neglecting the dynamic nuances of underwater conditions, leading to an inconsistent restoration outcome. To counter these impediments, we introduce an avant-garde Underwater Internet of Things (Underwater IoT) system, underpinned by a scene-depth fusion paradigm. Our methodology astutely accounts for the spectral decay of light underwater to infer a more refined attenuation coefficient tailored to the specific scene. This system, employing a quadtree decomposition for precise localization coupled with depth mapping, facilitates an astute estimation of prevailing luminescence. This depth map, once synthesized and refined, aids in gauging the precise attenuation dynamics of the aqueous milieu, culminating in a more precise transmission map derivation. Segueing from this, we employ an inverse model to refurbish the original image. Experimental results highlight our system’s prowess in counteracting issues like muddied details and chromatic anomalies while concurrently amplifying contrast. In juxtaposition with a spectrum of existing methodologies, our innovation outshines in terms of finesse and accuracy, underscoring its unparalleled efficacy in the challenging underwater conditions.

I. Introduction

The underwater research based on the extreme environment plays a vital role in the study of the ocean. The Underwater Internet of Things (Underwater IoT) system forms a crucial foundation for humanity’s exploration of the enigmatic depths of the ocean, offering a wealth of possibilities in areas, such as the collection of marine mineral resources, monitoring of the marine ecological environment, and various military applications. Nonetheless, owing to the light’s absorption and scattering in the aquatic medium, underwater images captured by Underwater IoT systems in extreme conditions frequently contend with issues like color distortion, blurred details, and inadequate contrast. This is because of the color distortion caused by the attenuation of the wavelength of light underwater, which increases with distance. Red light with longer wavelengths decays the fastest and is most affected compared to green and blue light. In addition, the scattering effect increases with distance, reducing the contrast of underwater images and causing blurring [1]. Therefore, finding an effective imaging method in Underwater IoT systems is of great significance for the perception of Marine information.