In previous work we described tonal enhancements by domain experts (biologists) to aid annotation of underwater seabed habitats. Tone maps were created using a typical, and interactive, curve manipulation GUI with a set of control points. These can be dragged to alter brightness and contrast. Such tools offer bespoke and targeted image enhancements, that are preferred over more general automatic tools, but are too time-consuming to produce for large datasets.
We found that a smoother and simpler approximation of these tonal manipulations could be derived using our Weibull Tone Mapping (WTM) algorithm. This involves fitting a Weibull Distribution (WD) to brightness histograms of input and user-adjusted output images, then solving for the tone map that mapped the underlying distributions to each other. This tone mapping operation (TMO) was preferred to their own bespoke adjustments, for identifying benthic habitats from imagery. WTM therefore provides the necessary building blocks to develop a targeted enhancement algorithm, that can quickly create smooth tonal manipulations.
In this work we explored how widely applicable the WTM algorithm is to underwater images, by focusing on a larger dataset. Specifically, we introduce WTM as a parameterized enhancement tool, in which analysts can specify a desirable target WD that an image can be rendered to, by modifying its two parameters. Under experimental conditions, 10 observers used WTM to enhance images to aid seabed habitat identification. In the event that a suitable WTM adjustment could not be found, observers could interactively manipulate the WTM tone map using an interactive curve tool with 6 moveable control points, until satisfied. We use this opportunity to further explore desirable TMOs and investigate the capability of WTM to simplify control point tone-mapping tools.
We demonstrate that given the choice, experts typically find a WTM enhancement sufficient for their analyses (81% of images) compared to an advanced adjustment from an interactive tool. Interestingly, in the latter cases, we find that the majority (91%) of TMOs could be approximated by our WTM algorithm, using mean CIE ΔE <5 as our threshold for success. Intra and inter-variability of observers was low and image content did not appear to influence observer tool choice.
These results further illustrate that the WD is a good model and target distribution of underwater image histograms. We see that WTM’s usage extends beyond simplification and smoothing of complex and time-consuming tonal manipulations, to a successful and preferred enhancement tool. This data provides the necessary groundwork to investigate whether a suitable WTM can be derived automatically from images.