The incident light will be scattered away due to the inhomogeneity of the refractive index in many materials which will greatly reduce the imaging depth and degrade the imaging quality. Many exciting methods have been presented in recent years for solving this problem and realizing imaging through a highly scattering medium, such as the wavefront modulation technique and reconstruction technique. The imaging method based on compressed sensing (CS) theory can decrease the computational complexity because it doesn't require the whole speckle pattern to realize reconstruction. One of the key premises of this method is that the object is sparse or can be sparse representation. However, choosing a proper projection matrix is very important to the imaging quality. In this paper, we analyzed that the transmission matrix (TM) of a scattering medium obeys circular Gaussian distribution, which makes it possible that a scattering medium can be used as the measurement matrix in the CS theory. In order to verify the performance of this method, a whole optical system is simulated. Various projection matrices are introduced to make the object sparse, including the fast Fourier transform (FFT) basis, the discrete cosine transform (DCT) basis and the discrete wavelet transform (DWT) basis, the imaging performances of each of which are compared comprehensively. Simulation results show that for most targets, applying the discrete wavelet transform basis will obtain an image in good quality. This work can be applied to biomedical imaging and used to develop real-time imaging through highly scattering media.
We address an optical imaging method that allows imaging, which owing to the “memory-effect” for speckle correlations, through highly scattering turbid media with “Error Reduction - Hybid Input Ouput (ER-HIO)” algorithm. When light propagates through the opaque materials, such as white paint, paper or biological tissues, it will be scattered away due to the inhomogeneity of the refractive index. Multiple scattering of light in highly scattering media forms speckle field, which will greatly reduce the imaging depth and degrade the imaging quality. Some methods have been developed to solve this problem in recent years, including wavefront modulation method (WMM), transmission matrix method (TMM) and speckle correlations method (SCM). A novel approach is proposed to image through a highly scattering turbid medium, which combines speckle correlations method (SCM) with phase retrieval algorithm (PRA). Here, we show that, owing to the “optical memory effect” for speckle correlations, a single frame image of the speckle field, captured with a high performance detector, encodes sufficient information to image through highly scattering turbid media. Theoretical and experimental results show that, neither the light source, nor wave-front shaping is required in this method, and that the imaging can be easily realized here using just a simple optical system with the help of optical memory effect. Our method does not require coherent light source, which can be achieved with LED illumination, unlike previous approaches, and therefore is potentially suitable for more and more areas. Consequently, it will be beneficial to achieve imaging in currently inaccessible scenarios.
A method to recover the image of an object behind a highly scattering medium with higher accuracy is presented. Instead of the Pearson correlation coefficient (PCC) used in the existing methods, structural similarity (SSIM), which is known as an excellent evaluation indicator of image quality, is employed as the cost function for the wavefront optimization. Compared to PCC, better imaging quality can be acquired with SSIM, because the latter comprehensively analyzes the luminance, the contrast, and the structure of imaging results. By comparing the performances of the three commonly used global optimization algorithms, including a genetic algorithm (GA), particle swarm optimization and differential evolution algorithm, we verify that GA has the best reliability and stability to solve this multidimensional wavefront modulation problem among these global optimization algorithms, including in strong noise environments. This work can improve the quality of imaging through a highly scattering medium with a wavefront optimization technique and can be applied to the fields of optical detection or biomedical imaging.
Multiple scattering of light in opaque materials such as white paint and human tissue forms a volume speckle field, will greatly reduce the imaging depth and degrade the imaging quality. A novel approach is proposed to focus light through a turbid medium using amplitude modulation with genetic algorithm (GA) from speckle patterns. Compared with phase modulation method, amplitude modulation approach, in which the each element of spatial light modulator (SLM) is either zero or one, is much easier to achieve. Theoretical and experimental results show that, the advantage of GA is more suitable for low the signal to noise ratio (SNR) environments in comparison to the existing amplitude control algorithms such as binary amplitude modulation. The circular Gaussian distribution model and Rayleigh Sommerfeld diffraction theory are employed in our simulations to describe the turbid medium and light propagation between optical devices, respectively. It is demonstrated that the GA technique can achieve a higher overall enhancement, and converge much faster than others, and outperform all algorithms at high noise. Focusing through a turbid medium has potential in the observation of cells and protein molecules in biological tissues and other structures in micro/nano scale.
Particle Swarm Optimization (PSO) is exploited in an optical focusing system, by changing the phase of the incident light, which can break the diffraction limit and enhance focal intensity through highly scattering media. To emphasize that the focusing optical system is mainly composed of a spatial light modulator (SLM), a lens and highly scattering media placed behind the lens. The stepwise sequential algorithm and the continuous sequential algorithm are sensitive to noise and the genetic algorithm converges slowly. Compared with these algorithms theoretically and experimentally, the PSO is robust, effective and able to converge rapidly, which obtains a best solution by following the search for the optimal particle in the solution space. The capacity of beyond-diffraction and increasing intensity of the focus through dynamic scattering media could be conducive to biological microscopy and imaging through turbid environments.