Dynamics of a speckle pattern formed on the surface of a diffusely reflecting object is a source of valuable information about the changes that occur in the object. Laser speckle photometry extracts relevant information about the object from variations in speckle intensity. Speckle dynamics is visualized by means of an activity map, which renders the 2D distribution of a statistical parameter related to intensity changes. The contrast of the map is crucial for better detection of areas of different activity. Strong fluctuations in the map input data severely degrade the contrast. The main factors affecting the contrast are the processing algorithm and the speckle intensity distribution defined by the parameters of the optical system. The aim of this work is to find the optimal speckle pattern for laser speckle photometry based on the quality assessment of a 2D activity map estimated by correlation-based algorithms with averaging in the temporal or spatial domain. The study included simulation of correlated images with symmetric/asymmetric intensity distribution or at different speckle contrasts. We checked the quality of the obtained map for the case of 8-bit encoded, binary, or JPEG-compressed speckle images. We performed sensitivity evaluation of the method for non-destructive testing of samples under tensile extension.
In this work, we propose to leverage a deep-learning (DL) based reconstruction framework for high quality Swept-Source Optical Coherence Tomography (SS-OCT) images, by incorporating wavelength (λ) space interferometric fringes. Generally, the SS-OCT captured fringe is linear in wavelength space and if Inverse Discrete Fourier Transform (IDFT) is applied to extract depth-resolved spectral information, the resultant images are blurred due to the broadened Point Spread Function (PSF). Thus, the recorded wavelength space fringe is to be scaled to uniform grid in wavenumber (k) space using k-linearization and calibration involving interpolations which may result in loss of information along with increased system complexity. Another challenge in OCT is the speckle noise, inherent in the low coherence interferometry-based systems. Hence, we propose a systematic design methodology WAVE-UNET to reconstruct the high-quality OCT images directly from the λ-space to reduce the complexity. The novel design paradigm surpasses the linearization procedures and uses DL to enhance the realism and quality of raw λ-space scans. This framework uses modified UNET having attention gating and residual connections, with IDFT processed λ-space fringes as the input. The method consistently outperforms the traditional OCT system by generating good-quality B-scans with highly reduced time-complexity.
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