Compressive light field (CLF) is a promising light field display technology, and the traditional multiplicative CLF limits the number of layers due to the low transmittance of liquid crystals, which results in a small depth of field. Therefore, this paper proposes a three-dimensional display structure with a hybrid CLF. This structure utilizes a semi-transparent and semi-reflective mirror to superimpose two sets of multiplicative CLFs, each of which consists of two identical liquid crystal displays and a uniform backlight. The hybrid CLF has a greater depth of field and higher brightness, further improving image quality. Due to the properties of the hybrid CLF structure and the non-negative tensor (NTF) decomposition algorithm, the reconstructed image can suffer from layered image crosstalk, which leads to image quality degradation. We propose a method to reduce the hybrid CLF layered image crosstalk, and we validate the proposed method through computer simulations and optical experiments.
As we all know, the traditional compressed light field 3D display technology has the problems of limited 3D depth of field and low display brightness. In this paper, a hybrid compressed light field device based on polarization multiplexing is proposed, which combines multiplicative and superimposed compressed light field 3D display to improve the light intensity perceived by human eyes and enlarge the depth of field. In addition, when using high-brightness mini-leds, noise can appear at the edges of the reconstructed image. This is because non-negative tensor matrix (NTF) algorithm adopts hierarchical iteration, which is easy to fall into the local optimal solution, resulting in poor optimization effect of the edge part and noise. Then we introduce the stochastic gradient descent (SGD) algorithm which can better improve the problem of edge noise because all spatial light modulator pixel values are updated at the same time in the iteration process. In terms of perception indicators, NTF uses the mean square error coefficient, which cannot account for many nuances of human perception, resulting in iterative results that sometimes do not conform to the subjective perception of human eyes. In contrast, the loss function of SGD can be self-defined. This paper introduces the Learned Perceptual Image Patch Similarity, which is more in line with human perception. Through simulation and experiments, we verify the advantages of the proposed device and the effectiveness of the corresponding optimization algorithm.
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