Digital holography facilitates registering and reconstructing information pertaining to 3D objects and scenes. Several constraints related to the quality of reconstructed images persist, including speckle noise, twin images, zero order, shot noise and dark temporal noise, camera fixed-pattern noise (spatial noise), dynamic range issues, and quantization noise. The aforementioned factors determine the maximum viable signal-to-noise ratio (SNR) of digital holograms and subsequent reconstructed images. Fluctuations in illumination engender noise in reconstructed images, thus constraining holographic applications in areas like 3D object detection and optical encryption.
This research scrutinizes the impact of major noise components inherent to digital cameras on hologram reconstruction. Mathematical derivation furnished analytical equations for appraising the SNR of reconstructed amplitude images. These equations elucidate the relationships between parameters such as shot noise, dark temporal noise, fixed-pattern noise, camera dynamic range, quantization noise, and the ratio between reference and object beam intensities alongside the ratio between object area and overall reconstructed field.
Experimental testing utilizing digitally acquired holograms of diffusely scattering objects and an array of CCD and CMOS cameras substantiated the precision of the formulated equations. The cameras encompassed digital single-lens reflex (DSLR), scientific, industrial, and surveillance cameras. The influence of camera noise on reconstructing phase images was also evaluated.
The obtained equations enable prefatory gauging of the impact from camera noise on the quality of reconstructed images. Consequently, optimal selection of illumination, exposure duration, object size, and other experimental parameters to minimize noise in holograms is facilitated, thereby enhancing the efficacy of holographic deployment in diverse areas including 3D object detection and optical encryption.