  The integration of deep learning techniques with biophotonic setups has
opened new horizons in bioimaging. A compelling trend in this field involves
deliberately compromising certain measurement metrics to engineer better
bioimaging tools in terms of cost, speed, and form-factor, followed by
compensating for the resulting defects through the utilization of deep learning
models trained on a large amount of ideal, superior or alternative data. This
strategic approach has found increasing popularity due to its potential to
enhance various aspects of biophotonic imaging. One of the primary motivations
for employing this strategy is the pursuit of higher temporal resolution or
increased imaging speed, critical for capturing fine dynamic biological
processes. This approach also offers the prospect of simplifying hardware
requirements/complexities, thereby making advanced imaging standards more
accessible in terms of cost and/or size. This article provides an in-depth
review of the diverse measurement aspects that researchers intentionally impair
in their biophotonic setups, including the point spread function,
signal-to-noise ratio, sampling density, and pixel resolution. By deliberately
compromising these metrics, researchers aim to not only recuperate them through
the application of deep learning networks, but also bolster in return other
crucial parameters, such as the field-of-view, depth-of-field, and
space-bandwidth product. Here, we discuss various biophotonic methods that have
successfully employed this strategic approach. These techniques span broad
applications and showcase the versatility and effectiveness of deep learning in
the context of compromised biophotonic data. Finally, by offering our
perspectives on the future possibilities of this rapidly evolving concept, we
hope to motivate our readers to explore novel ways of balancing hardware
compromises with compensation via AI.
