The absorption and scattering properties of light in biological tissues reflect their key characteristics. However, strong background light often obscures these properties. Currently, contrast enhancement in imaging primarily focuses on noise reduction from an image processing perspective rather than considering the properties of the light field itself. This approach has limitations in preserving imaging details and information, particularly under strong background light conditions, leading to information loss. This paper introduces an imaging technique based on the Low-rank Sparse Matrix Decomposition (LSMD) to achieve high-quality imaging of biological tissues. Experiments in Gaussian and ring-shaped light fields validate that the low-rank component of the light field represents background light, while the sparse component indicates the scattering and absorption properties of the target. This technique effectively highlights microscopic details and scattering/absorption characteristics. Compared to traditional image-based filtering methods, this light field-based technique significantly improves the extraction of absorption and scattering information. It has the potential to enhance tissue visualization in medical diagnostics and research.
The effect of different interpolation methods on imaging quality of swept-source optical coherence tomography (SSOCT) is analyzed. A 1310nm SS-OCT system is built for imaging. Time-domain interpolation method based on spectral phase is used for resampling. The interpolation methods include piecewise linear interpolation, piecewise quadratic parabola interpolation, piecewise cubic Hermite interpolation and piecewise cubic spline interpolation. The image of the plane mirror, human skin and eyes are compared by the above four interpolation methods. Experimental results demonstrate that the piecewise cubic spline interpolation has the best image quality, which is the smoothest and has the lowest noise.
We introduce a new method according to the reflection matrix algorithm to restore the image of the scattering medium with a spatial resolution of 1.99μm. Our principle is based on the low coherence interferometer. First, the interference fringe patterns formed on the beam splitter can be captured by CCD sensor. Then, the SVD method applied on reflection matrix can separate single scattered photons. Thus, the reconstructed pattern is obtained. The proposed image reconstruction method is expected to have a prominent impact on deep-tissue imaging and the study of the physics of the interaction of light with complex media.
Optical coherence tomography (OCT) has been an important diagnosis tool for ophthalmic diseases because of its non-invasive and high-resolution feature. The image quality can effectively decrease because of the jitter from human body and eye, especially the distortion of the 3d reconstructed retinal images. A correction method for three-dimensional OCT retinal image based on curve fitting is proposed in this paper. The OCT retina image boundary is extracted and fitting by the preprocessing. Through frame sampling and curve fitting, the offset of each frame slice image is calculated. The experimental results show that the distortion of the OCT retina three-dimensional reconstruction image can be significantly corrected by the proposed method.
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