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Light field imaging plays a vital role in three-dimensional reconstruction, synthetic aperture de occlusion, and holographic imaging. The light field dense reconstruction algorithm can make up for the shortage of light field imaging hardware and realize the dense reconstruction of the sparse light field. Based on the basic principle of two-dimensional optical flow and the light field biplane model, a new mathematical model of a four-dimensional light field optical flow constraint equation is proposed in this paper. The position coordinates of the new viewing angle are determined by using the light field optical flow solved by the constraint equation, and the intensity values of the new coordinates are obtained point by point through interpolation calculation and image inversion, so as to finally obtain a high-quality new viewing angle synthetic images. The experimental results show that the proposed method can realize high-quality reconstruction of the texture, shadow, and color information in the extended baseline scene. The quantitative evaluation results show that the algorithm can accomplish the task of dense light field reconstruction in complex settings. This algorithm in this paper is only applicable to the case of linear optical flow constraint and one-dimensional viewing angle light field. The subsequent related research will focus on the case of nonlinear optical flow constraint and multi-dimensional viewing angle light field.

Dual energy computed tomography (DECT) plays an important role in the application of advanced imaging due to its material decomposition capability. Image domain decomposition can directly invert CT images through by linear matrix, but the decomposed material images will be seriously affected by noise and artifacts. Although various regularization methods have been proposed to solve this problem, they still face two challenges: tedious parameter adjustment and the loss of image details resulted from over-smoothing. Therefore, in this paper we proposes a dual energy CT image material decomposition algorithm based on iterative residual network. Direct inversion is used as the initial base image, and a stacking two-channel convolutional neural network is used to replace the regularization items in the iterative decomposition model to form a deep iterative decomposition network. This method can realize material decomposition and noise suppression simultaneously. Experimental results show that the iterative residual network proposed in this paper is superior to other comparison methods and can effectively suppress noise and artifacts while maintaining the edge details of the base image.