Assessment of speckle denoising filters for digital holography using subjective and objective evaluation models

Digital holography is an emerging imaging technique for displaying and sensing three-dimensional objects. The perceived image quality of a hologram is frequently corrupted by speckle noise due to coherent illumination. Although several speckle noise reduction methods have been developed so far, there are scarce quality assessment studies to address their performance, and they typically focus solely on objective metrics. However, these metrics do not reflect the visual quality perceived by a human observer. In this work, the performances of four speckle reduction algorithms, namely, the nonlocal means-the Lee, the Frost, and the block-matching 3D filters, with varying parameterizations-were subjectively evaluated. The results were ranked with respect to the perceived image quality to obtain the mean opinion scores using pairwise comparison. The correlation between the subjective results and 20 different no-reference objective quality metrics was evaluated. The experiment indicates that block-matching 3D and Lee are the preferred filters, depending on hologram characteristics. The best-performing objective metrics were identified for each filter.