Due to the limitations of micro lens arrays and camera sensors, images on display devices through the integral imaging microscope systems have been suffering for a low-resolution. In this paper, a resolution-enhanced orthographic-view image display method for integral imaging microscopy is proposed and demonstrated. Iterative intermediate-view reconstructions are performed based on bilinear interpolation using neighborhood elemental image information, and a graphics processing unit parallel processing algorithm is applied for fast image processing. The proposed method is verified experimentally and the effective results are presented in this paper.

The application of recognition for the weld seam and its initial position in a complicated environment based on the image pattern match technology has been discussed. According to adopting the two-step pattern match method, the recognition speed and accuracy have been improved, and the error points have been wiped off during the global pattern match stage by using the area analysis method. The initial position can be accurately recognized by using the method that dynamically adjusts the search area during the local pattern match stage in the area that is near the end of the weld. The results of the experiments prove that this method not only has the characters that include anti-jamming, fast running speed and high recognition, but also has a preferably practical value.

Holographic optical element has a high diffraction efficiency and a narrow-band frequency characteristic, and it has a characteristic that is able to implement several features in a single flat device. It is widely applied in various fields. In this chapter, the principle and characteristics of the holographic optical elements are described in detail, and few typical holographic optical element-based applications, such as head-mounted display, lens array, and solar concentrator, are introduced. Finally, the futuristic research concepts for holographic optical element-based applications and contents are discussed.

A novel resolution-enhancement method for an integral imaging microscopy that applies interpolation and deep learning is proposed, and the complete system with both hardware and software components is implemented. The resolution of the captured elemental image array is increased by generating intermediate-view elemental images between each neighboring elemental image, and an orthographic-view visualization of the specimen is reconstructed. Then, a deep learning algorithm is used to generate maximum possible resolution for each reconstructed directional-view image with improved quality. Since a pretrained model is applied, the proposed system processes the images directly without data training. The experimental results indicate that the proposed system produces resolutionenhanced directional-view images, and quantitative evaluation methods for reconstructed images such as the peak signal-to-noise ratio and the power spectral density confirm that the proposed system provides improvements in image quality.

Abstract. Image fusion technology usually combines information from multiple images of the same scene into a single image so that the fused image is often more informative than any source image. Considering the characteristics of low-light visible images, this study presents an image fusion technology to improve contrast of lowlight images. This study proposes an adaptive threshold-based fusion rule. Threshold is related to the brightness distribution of original images. Then, the fusion of low-frequency coefficients is determined by threshold. Pulsecoupled neural networks (PCNN)-based fusion rule is proposed for fusion of high-frequency coefficients. Firing times of PCNN reflect the amount of detail information. Thus, a high-frequency coefficient corresponding to maximum firing times is chosen as the fused coefficient. Experimental results demonstrate that the proposed method obtains high-contrast images and outperforms traditional fusion approaches on image quality. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.