Light scattering is inevitable in optical imaging and random speckles will be formed when light passes through the scattering media due to the influence of multiple scattering. Recent research results show that the super-resolution imaging can be achieved by the scattering effect.In this work, the effective focusing of the laser beam through the is realized when the wavefront of the incident light is modulated by the feedback optimization algorithm (area-by-area modulation algorithm) with the spatial light modulator. The experiment results indicate that the maximum light intensity growth factor (132 in this work) and the focusing spot size (1/10 compared to the traditional lens focusing) are dependent on the total number of modulation units on the spatial light modulator and the phase accuracy of each modulation unit. In particular, the focusing of a vector beam through the scattering media provides a new way to control the the maximum light intensity growth factor and the focusing spot size.
The image reconstruction of an object passed through a scattering media has attracted a lot of interest due to its potential application in corresponding fields. Recently, the deep learning techniques have been introduced into the computational imaging through scattering media and obtained good results. In this work, a modified U-Net model with dense blocks is designed under the framework of PyTorch, MobileNet is used as the backbone model. The network is trained by using mean square error (MSE) loss function. The features of image can be extracted and the information of every pixel of the speckle field can be classified and restored in this model through depth separable convolution. Thus, the speckle field can be reconstructed. The experimental results show that this network has good generalization ability for image reconstruction and improves the ability of information acquisition.
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