Subpixel based defocused points removal in photon-limited volumetric dataset

Roles of Deep Learning in Optical Imaging

“…The defocused points in the 3D sectional image do not convey any valuable information and are therefore redundant. Recently, we have demonstrated a way to manually identify and remove the off-focus points from a 3D sectional image [11]. Furthermore, under some special imaging scenarios (e.g., biomedical imaging and night vision), low light levels or photon-starved illumination conditions may be encountered.…”

“…Several studies have been conducted to demonstrate the feasibility of combining photon detection imaging or photon counting imaging (PCI) techniques with conventional 3D integral imaging systems, known as photon counted integral imaging (PCII) [2,[9][10][11]17]. In such systems, it is known that the reconstructed depth images contain both the focused and off-focus (or out-of-focus) voxels simultaneously (see for instance Figure 3).…”

“…Off-focus pixels often look blurred and therefore do not convey clear information about the scene. Several approaches have been proposed to efficiently remove the off-focus points from the reconstructed 3D images [4,11]. We note that the existing approaches are subjective as they involve manual calculation of algorithm parameters such as variance, threshold, etc., which is time consuming.…”

A Deep Learning Framework to Remove the Off-Focused Voxels from the 3D Photons Starved Depth Images

“…Previously, we also demonstrated a subpixel-level three-steps-based statistical approach to efficiently remove the off-focused points from the 3D sectional images in color (RGB) format [17]. We note that both of these previous approaches are subjective, as they involve performing manual calculations of algorithmic parameters such as mean, variance, threshold, etc., which is time-consuming and also varies according to the scene [17].…”