This paper presents a region-based technique for fusion of a multifocus color image sequence in the LUV color space. First, mean shift segmentation was applied on the weighted average image of the image sequence to obtain the fusion reference areas. Second, for each segmented area, the well-known modified Laplacian (LAP2) was used as a focus measure to select the clearest parts within the image sequence and then a final image focused with all parts can be generated. Mutual information, QAB/F metric, entropy, standard deviation, image sharpness metric, image contrast metric, average gradient, and spatial frequency were adopted to assess the quality of the fused image. Experiments carried out using standard image sequences from HeliconSoft demonstrated that the results obtained through our technique offer good performance. The proposed technique can be used to extend the depth of field (DOF) of a camera system effectively.

To realize the large-scale and high-precision co-phasing adjustment of synthetic-aperture telescopes, we propose a multichannel left-subtract-right feature vector piston error detection method based on a convolutional neural network, which inherits the high precision and strong noise resistance of the DFA-LSR method while achieving a detection range of (-139 lambda, 139 lambda) (lambda = 720 nm). In addition, a scheme to build large training datasets was proposed to solve the difficulty in collecting datasets using traditional neural network methods. Finally, simulations verified that this method can guarantee at least 94.96% accuracy with large samples, obtaining a root mean square error of 10.2 nm when the signal-to-noise ratio is 15. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

The Richardson–Lucy (RL) algorithm is a well-known nonlinear restoration method and has been widely applied in the fields of astronomical image restoration, microscopic image restoration, and so on because of its capability of generating high-quality restoration results and potential in realizing super-resolution. However, when being applied to restore the wavefront coded blurry images, the classical RL algorithm converges very slowly and has to be iterated many times before obtaining a satisfactory result, which severely prohibits its real-time application. Vector-extrapolation-based RL algorithm was invented to solve this problem, but the noise amplification increases fast, and additional post-processing is needed to further improve the signal-to-noise ratio. Therefore, in this paper, an improved RL algorithm is proposed by introducing an exponential modified correction term into the framework of the original vector-extrapolation-based RL algorithm. It not only results in a bigger iteration step, which ensures a faster convergence can be obtained, but also the noise amplification is effectively prohibited. Besides that, we design a structure-similarity-index-metric-based stopping criterion, based on which the optimum number of iterations for each color channel is obtained. Experimental results reveal that the total iterations decreases approximately 78.9%, and the restored images demonstrate a superior visual quality without denoising additionally.

Fourier ptychography imaging is a powerful phase retrieval method that can be used to realize super-resolution. In this study, we establish a mathematical model of long-distance camera scanning based on reflective Fourier ptychography imaging. In order to guarantee the effective recovery of a high-resolution image in the experiment, we analyze the influence of laser coherence in different modes and the surface properties of diverse materials for diffused targets. For the analysis, we choose a single-mode fiber laser as the illumination source and metal materials with high diffused reflectivity as the experimental targets to ensure the validity of the experimental results. Based on the above, we emulate camera scanning with a single camera attached to an X-Y translation stage, and an experimental system with a working distance of 3310 mm is used as an example to image a fifty-cent coin. We also perform speckle analysis for rough targets and calculate the average speckle size using a normalized autocorrelation function in different positions. The method of calculating the average speckle size for everyday objects provides the premise for subsequent research on image quality evaluation; meanwhile, the coherence of the light field and the targets with high reflectivity under this experiment provide an application direction for the further development of the technique, such as computer vision, surveillance and remote sensing.

The echelle grating spectrometer, with a wide spectral range and high-resolution spectral analysis, is one of the best tools for fine spectral measurement. Nevertheless, it suffers from excessive residual aberrations and a large overall size. In this study, the design and implementation of a novel asymmetric Czerny–Turner ultra-wide spectral range achromatic echelle spectrometer are described. The echelle spectrometer has three channels, and it uses an off-axis parabolic mirror to obtain collimated light without aberrations. Three sets of gratings and dispersive prisms with different coatings are utilized as cross-dispersion elements to acquire two-dimensional images containing spectral information. Suitable detectors are selected according to the requirements of each channel, and three sets of coaxial focusing lenses are designed separately to minimize the aberration. The results of the simulation analysis by ZEMAX indicate that in the entire operating band (200–1100 nm), the root mean square radius of the dispersion spots is ≤ 2.2 μm, all of which are located within the limited range of the size of the detector, thus ensuring that the system’s spectral resolution reaches 0.02 nm at 200 nm, 0.04 nm at 650 nm, and 0.1 nm at 1100 nm.