Iceberg distribution, dispersion, and melting dynamics are pivotal in regulating the Ocean's heat and freshwater balance. However, deciphering these dynamics is a formidable challenge. In visible imagery, icebergs present significant identification challenges due to their variable appearances, which are influenced by many environmental conditions. These variations manifest as differences in color, texture, shape, and size, complicating the accurate discrimination of icebergs from open water or sea ice. Thus, developing reliable detection methods is critical for monitoring iceberg trajectories, disintegration patterns, and their consequent impact on oceanic freshwater influx. The essence of iceberg detection in visible imagery is the ability to differentiate these formations from their surrounding aquatic environment. Iceberg features display a spectrum of visual characteristics shaped by factors such as meteorological conditions, sea states, and the physical properties of the iceberg surfaces. As a result, adaptive imaging techniques are essential for efficacious detection. This study introduces an innovative Adaptive Contrast Enhancement framework meticulously crafted for iceberg detection in visible imagery. Utilizing a parameterized logarithmic model inspired by the Retinex theory, this method enhances the isolation and manipulation of image elements, thereby significantly elevating image quality. Our findings reveal that this technique markedly improves the visibility of icebergs, outshining traditional and contemporary detection methodologies. Furthermore, it affords more profound insights into the dynamic interplay of icebergs within the marine ecosystem.