Since capturing underwater images without degradation is challenging, there are few real image datasets with paired ground truth for underwater image enhancement. In this article, we propose a generative adversarial network (UIGAN) for underwater imaging; the network can convert images and their corresponding depth maps captured in air into images in water. The underwater imaging mechanism relies on many intrinsic parameters in water, which are difficult to estimate without field calibration. As the strong modeling capability of deep neural networks, this article uses the deep learning model to extract parameters from the real underwater environment. Then the proposed UIGAN simulates the light propagation process (direct attenuation, backscattering, and forward scattering) in water by using three modules with different constraints. We can generate a large training dataset with paired images in air and real water environment. The generated UIGAN dataset serves as input to a forward-attention transfer underwater enhancement model (FATUECNN), and it can output the restored images with appearance like those captured in air. The proposed pipeline is verified both qualitatively and quantitatively by extensive experiments and comparison evaluation with the existing state-of-the-art methods. The source code and the pre-trained model are made publicly available.

Underwater image restoration is one of the significant research in marine engineering and aquatic robotics. However, due to the propagation characteristics of light and the serious turbidity in underwater, the captured images often have chromatic aberration and scattering blur, which brings great challenges to the restoration of the raw image. In this paper, a revised underwater imaging model is proposed first, which reanalyzes the generation of background light from the atmosphere to the underwater and provides important support for underwater color correction. And then a network framework via the revised model is designed, which can decompose the captured image into different components corresponding to the revised model. The proposed network consists of a decomposition architecture with residual blocks that learns a complete separation of clear image and transmittance features. These two features are used along with the raw image to predict the background light. Finally, combining three constraints of the imaging model, the proposed framework can converge rapidly along the desired direction. By comparison with the performance of the state-of-the-art algorithms, the designed network shows excellent visibility and is capable of removing water on both synthetic and real-world images in different water types.

Quantitative phase imaging (QPI) technology is one of the important techniques for nondestructive imaging of a cell’s morphology. In QPI, the decoupling of refractive index (RI) and thickness of a cell is a key problem. We aim at the multiple hypothesis constraints (MHC) shortcomings of the traditional method in the decoupling of multi-media and nonspherical cells. A method is proposed to decouple the RI and the thickness of a cell under three-wavelength phase imaging in this paper. In this method, the sample’s RI and thickness distributions can be obtained by solving three-wavelength phase equations using the approximate setting under dispersion based on the optical dispersion theories. In simulation experiments, the results show that the method can effectively decouple the RI and thickness for homogeneous and multi-media nonspherical cells and has high accuracy. Because the method in this paper is obtained by using few approximate assumptions and strict mathematical analysis, it can decouple the RI and thickness of nuclear cells with any shape, and moreover, it provides an important basis for the technological development of cell morphology analysis.

The three-dimensional reconstruction from single input image is quite difficult due to many unknown parameters, such as the light conditions, the surface normal and albedo of the object. However, there are overall similar characteristics for different human faces, such as the shapes and the positions of the eyes, nose; mouth and ears are generally identical. The similar characteristics has been used in this paper to relax the numbers of the input face images, and reconstruct the 3D shape based on a couple statistical model. Moreover, the light condition of the single input image can be different from that of training database. The experiment results show the effectiveness of the proposed method.

The lengths of binary Golay complementary pairs are limited to 2α10β26γ, where α, ß, γ ∈ {0, 1, 2, . . .}. This weakness is repaired by the Z-complementary pairs (ZCPs). In this letter, by concatenating several different complementary pairs of length 2m, we present a construction of even-period binary ZCPs of length N = 2m+ 3+ 2m+ 2+ 2m+ 1with zero-correlation zone (ZCZ) width Z = 2m+ 3. We also propose another construction of ZCPs of length N' with larger ZCZ width Z = 6N'/7.