With the rapid development of information technology, people's demands for large-angle coverage and high-information acquisition of optical systems are increasing in industrial production and daily life. Therefore, the requirements for imaging quality of wide-angle lenses are improving. Aiming at solving the severe aberrations in the design of the wide-angle lens, traditional optical designs often introduce complex structures. However, a complex structure will bring several drawbacks such as large volume, heavyweight and high cost. Computational imaging technology(CIT) takes information-driven as the core, breaks the imaging mode of independent optimization of optical design and image restoration, realizes global integrated optimization design, and breaks the limit of traditional imaging. Based on the CIT theory, a simple wide-angle optical system design method is proposed and demonstrated in this paper. Firstly, in the optical design process of the wideangle lens, the requirements for suppressing aberrations are relaxed, and the image quality is worse. Then, the cross-channel image restoration algorithm is used to remove the residual aberrations to obtain a high-quality image. Finally, the system is simplified. This method can not only obtain high-quality images but also reduce the complexity of a wide-angle optical system.
Aiming at correcting the severe chromatic aberration of the long focal lens, the traditional optical design often requires employing complex structure, introducing special dispersive glasses or even utilizing the hybrid refractive-diffractive imaging method. However, complex structure will bring several drawbacks such as large volume and heavy weight. Special glasses and refractive-diffractive hybrid imaging will greatly increase the cost, which hardly meets the needs of miniaturization and low cost of optical imaging module. Although image restoration algorithm is commonly used to optimize the image quality to a certain extent, the optical design and image restoration process are independent of each other. Therefore, it is difficult to ensure the high resolution of the image while realizing the light weight, small volume and low cost of the optical system simultaneously. Utilizing the computational imaging theory, a simple long-focus optical system design method based on the optical/image co-design is proposed and deomonstrated in this paper. On the basis of the idea of global optimization, the reported approach considers the two independent links of optical design and image restoration as a combination. The imaging quality requirements in the optical system design are relaxed at the front end, and the image restoration algorithm is used to remove the residual aberrations in the back end. This method can not only obtain the same or even higher imaging performance, but also reduce the complexity of the optical system.
The speckle noise in the imaging process of medical ultrasound imaging will be mixed with effective information, which will reduce the image quality and affect the doctor's diagnosis. Therefore, it is of great significance to study the denoising method of medical ultrasound images. Guided image filtering is a kind of edge-preserving algorithm, which can smooth the image at the same time reserving the edge of the image. However, because guided image filtering is insensitive to texture details, it can result in the loss of detailed information of the medical ultrasound image, and the fractional differential method can just compensate for this disadvantage. In order to reserve the edge features and texture features of medical images while removing noise, we propose a denoising method of medical ultrasound image based on guided image filtering and fractional derivative. Firstly, we logarithmically transform medical ultrasound images so the multiplicative noise is convert into additive noise. Then, in order to retain the detailed information of the medical ultrasound image, it is necessary to enhance its sensitivity to the texture details of the guide filter. In this paper, the image is processed with a fractional differential mask to obtain enhanced texture information, which is then imported into the guided image filter. Next, the medical ultrasound image is processed using the guided image filter containing texture information, and finally an exponential transformation is performed to obtain a denoised image. Through experiments, we can conclude that the proposed algorithm not only can effectively enhance the visual effects of ultrasound images while removing noise, but also can effectively preserve edge and texture information.
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