Image restoration has attracted the attention of many scientists due to the image is degraded by the bad weather (such as haze, smog, fog). Clear images provide a means for security surveillance, remote sensing and various military application to understand objective facts. Many dehaze methods have been proposed by the experts for image restoration, especially for image dehaze. The dark channel prior dehaze method is a typical image restoration method based on atmospheric physical model. This method is a kind of statistics of outdoor haze-free images, and it is a simple but effective remove haze from a single input image. However, this method fails to restore the sky region of degraded image, and it has a high computational cost associated with soft matting algorithm. To overcome these problems, we propose an image restoration method based on quadtree decomposition to restore the images degraded by scattering media. The proposed method uses the quadtree decomposition to find the sky region for the atmospheric light estimation. The transmission of sky region is improved by the proposed method to obtain an accurate global transmission of degraded image. The degraded image can be quickly restored by our proposed method without halo effect or color distortion. The proposed method will be helpful to the security surveillance, remote sensing and various military application et al.
Noninvasive object imaging through strongly-scattering turbid layers has attracted the attention of many experts due to the potential application in the biomedical imaging and bioscience. The traditional speckle correlation method with Gerchberg-Saxton (GS) algorithm is used to restore object in a single-shot speckle pattern. However, this method suffers from the problems of convergence to local minimums, many iterations and cannot determine the object direction, due to the randomly assign initial value to GS algorithm. Bispectrum analysis method enables the directional Fourier phase retrieved using single-shot speckle pattern, but there are the problems of requiring high-resolution speckle pattern, low SNR and selecting the size of filter window. Therefore, we report an effective noninvasive imaging method through strongly-scattering turbid layers on the basis of bispectrum analysis and GS algorithm to restore the object in a lowresolution speckle pattern. Meanwhile, the new expression of Gaussian filter function is introduced contribute to determine the window size of filter in the processes of bispectrum analysis. In this proposed approach, the window size of filter is determined by the adjust factor according to the new form of Gaussian filter function, and the initial Fourier phase with directional information is generated by bispectrum analysis in a low-resolution speckle pattern. Then the initial Fourier phase is used as the randomly assign initial value to retrieve Fourier phase of object. Hence, the proposed method required no high-resolution, multiple iterations, nor randomly assign initial values to restore directional object. This work carries out simulations and experiments to demonstrates noninvasive object imaging in the low-resolution speckle pattern through strongly-scattering turbid layers.
3D localization of point source is widely used in many fields, such as bioimaging and autonomous driving fields. However, the localization is hard to perform under scattering conditions because of the diffuse effect of the scattering. We propose a novel method for 3D localization of point source under scattering conditions based on light field imaging and deep learning by only one shot. First, we introduce the description of the point source in a light field wise and how to localize a point source by its light field. On the basis, we elaborate on the effect of scattering on a light field and how to retrieve the location of a point source from a light field with scattering. Then, the effect of aberration on a light field will be introduced. We also build an artificial scene and a deep learning framework to perform a 3D localization practically, and the feasibility and accuracy of our method have been evaluated.
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