Multi-aperture optical systems provide a solution that can enhance resolution without the requirement for a large-diameter single-aperture system. However, one of the challenges of multi-aperture optical systems is the detection of the piston. The phase diversity (PD) technique can detect non-continuous co-phase errors and is often used for the detection of multi-aperture piston. The PD technique estimates the wavefront aberration of the optical imaging system and the target image by acquiring an image of the focal plane of an unknown target passing through the optical system and one or more images of known aberration (often chosen to be defocused). The PD technique is usually converted to a nonlinear optimization problem, but the optimization process may fall into local minima due to 2π piston ambiguity. Such a 2π piston ambiguity problem can be solved by using broadband light with multiple wavelengths. In this paper, a multi-wavelength phase diversity technique based on optimized grid search is used, which improves the detection range so that the piston and the final evaluation function values will be more likely to be within the correct range, and improves the solution success rate compared to the unoptimized grid search method.
The camera is an important part of the optical telescope observing system, and the performance of the camera is an important factor affecting the quality and efficiency of astronomical observations. EMCCD can achieve lower noise and higher detection sensitivity by charge multiplication techniques, and can be used to realize direct observations of very faint and weak targets, and relative to the traditional CCD/CMOS detectors, the noise level can be reduced by an order of magnitude to reach the Sub-electron level. However, facing the need for calibration of ultra-low noise at the sub-electron level, it is difficult to satisfy the currently available equipment and methods. Therefore, the study of EMCCD readout noise calibration method under high gain is of great significance for the theoretical study of EMCCD and the design of low-noise electronics. In this paper, we propose a calibration system of "cascaded integrating sphere + parallel light pipe" local illumination in dark room environment, through which we can obtain the light source under ultra-low brightness, which solves the problem of difficult to obtain the point light source, and the method of local illumination can avoid the fatigue attenuation problem under the high-fold gain, and we also refine the noise model, and propose a "gain-noise" model, which can be used to calibrate the EMCCD readout noise. The noise model is refined and the "gain-noise" fitting method is proposed, and finally the readout noise test at high gain achieves a noise calibration result of about 0.8e@600x.
Low-light remote sensing technology is crucial for surface observation during twilight and lunar phases; however, the acquired images often suffer from low contrast, low brightness, and low signal-to-noise ratios, which adversely affect observation quality. Traditional low-light image enhancement algorithms, such as Histogram Equalization, Gamma Correction, and Adaptive Histogram Equalization, can improve visual outcomes but also suffer from issues such as over-enhancement, loss of detail, noise amplification, and insufficient adaptability. To address these limitations, this paper proposes a low-light remote sensing image enhancement method based on Zero-Reference Deep Curve Estimation (Zero-DCE). This approach does not require paired samples and guides network learning through a non-reference loss function, making it particularly suitable for enhancing remote sensing images in low-light environments. Due to the lack of dedicated low-light remote sensing datasets, this study utilizes images from the UCMerced dataset to create simulated low-light remote sensing images for model fine-tuning. All color images are converted to grayscale to align with the characteristics of satellite-based low-light remote sensing images and to simplify the training process. Experimental results demonstrate that the proposed method significantly outperforms traditional techniques in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), while also excelling in denoising and preserving texture authenticity. The optimized Zero-DCE++ not only maintains the original performance but also significantly reduces computational costs and enhances inference speed, which is of great importance for real-time low-light remote sensing image processing on satellite platforms.
It is difficult for traditional CMOS camera to obtain clear images under extremely low-light conditions for example the new moon or the quarter moon because the photons generated are so few that the signal-to-noise ratio (SNR) is much lower than what is necessary to resolve finer details. Being different from traditional CMOS camera, intensified CMOS, named as ICMOS camera can greatly amplify the very limited arriving photons through external photoelectric effect and thus the corresponding SNR could be improved a lot for low-light conditions. In previous studies, by fusing a series of low-light images having sub-pixel displacement between each other through classical iterative back projection (IBP) reconstruction algorithm, not only the resolution is enhanced but also the SNR increases as well. However why the SNR can be improved through super-resolution reconstruction is not theoretically answered yet. Therefore in this manuscript two contributions are made. In the first place, the characteristics of sub-pixel super-resolution low-light imaging are firstly further investigated. By introducing the concept of spectral SNR, the analytical expression of the SNR before and after super-resolution reconstruction is established, based on which it is concluded that the MTF boosting generated by super-resolution reconstruction is one important factor that can bring in the SNR increment besides inherent noise reducing characteristic of the super-resolution reconstruction algorithm itself. In the second place, by combing the IBP based super-resolution reconstruction algorithm, the FFT (Fast Fourier Transform) based single image amplification and image enhancement methods together, better reconstruction results could be obtained.
A spherical coded imaging system combined with a controlled spherical aberration lens system and a digital sharpening filter can realize a fast and low-cost extended depth of field (EDoF) imaging system. At the same time, the wavefront coding technology is introduced, which can not only extend the depth of focus of the system, but also suppress the aberration including spherical aberration in the system design. However, for the wavefront coding system, due to the modulation of the incident light wave, the light distribution is more diffuse, so the blurred image generated by the wavefront coding system is a blurred image. It is necessary to decode and restore the intermediate blurred image to obtain a clear target image. In view of the lack of convergence and reliability of IBD algorithm, the Richardson-Lucy(RL) algorithm is introduced into RL-IBD algorithm, which can effectively reduce the sensitivity of the algorithm to noise. On the basis of vector extrapolation and exponential correction, this paper proposes improvements to the RL-IBD algorithm, which enhances the stability of the algorithm, and improves the convergence speed, noise suppression ability and adaptability of the algorithm.
By capturing a series of low-resolution images which have known or unknown sub-pixel displacement between each other, high resolution image could be reconstructed through algorithms such as IBP, POCS and so on. This technique mainly aims to solve the problem of aliasing effect caused by under-sampling but one problem exists. While applying sub-pixel shift based super-resolution reconstruction, point spread function is used to simulate the imaging process but usually the point spread function corresponding to the low-resolution imaging system is used, which does not match reconstruction in high-resolution grid. According to our previous researches, the wave-front coding technique could be used to realize single image amplification based super-resolution reconstruction because the point spread function corresponding to the high-resolution grid could be digitally generated in a more accurate way. In this manuscript, the rotationally symmetric wave-front coding technique and the sub-pixel shift based super-resolution imaging are combined together and there are two advantages. First, because of decrease of the magnitude of optical transfer function caused by wave-front coding, the aliasing effect in the intermediate images is reduced keeping pitch size unchanged. Second, while doing the reconstruction in high-resolution grid, the computed point spread function corresponding to the high-resolution grid is used, which better matches the high-resolution grid. The numerical results demonstrate that better image could be obtained by incorporating rotationally symmetric wave-front coding into sub-pixel shift based super-resolution imaging.
Phase diversity technique (PD) can jointly estimate the wavefront aberration and the target image of an optical imaging system. The PD technique reconstructs images by acquiring a focal plane image of optical system and one or more images with known aberrations (often selected defocus). Due to the simple construction of the optical system, the ability to detect discontinuous co-phase errors, and its applicability to both point sources and extended targets, The PD technique is uniquely suited for spatial target imaging applications, especially for the detection of multi-aperture piston errors. However, in a spatially low-illumination environment, Poisson noise as the main noise source of the imaging system seriously affects the accuracy of the reconstructed images. In this paper, we propose a method of phase diversity technique based on a fast Non-local Means (NLM) algorithm for reconstructing single-aperture images or multi-aperture images. For the two cases of single-aperture imaging and multi-aperture imaging with piston errors in spatial low illumination conditions, the method is used to solve the sensitivity problem of Poisson noise during image reconstruction. Numerical simulation results show that our method has significant improvement in structural similarity of the recovered images compared with the traditional phase diversity technique, and also is faster than the common non-local mean algorithm. The combination of this fast non-local means algorithm which using integral images and the phase diversity technique greatly reduce the computation time. The field experimental results and simulation results show good agreement. The new method would be useful in the AO system with active Poisson noise.
It is difficult for normal CCD or CMOS camera to obtain high quality images under extremely low-light conditions for example the new moon or the quarter moon because the photons arriving at the detector are so few that signal to noise ratio (SNR) is much lower than what is necessary to resolve finer details in the nighttime scenario. To solve this problem, the intensified CCD or CMOS camera is adopted and the few photons is amplified to improve the SNR a lot. However, the intensifier is mainly composed of the cathode, MCP (Micro-channel-plate) and fluorescent screen and this complex structure and the multiple photoelectric conversion during the photon amplification process will lead to a big equivalent pitch size, which degrades the spatial resolution. Therefore in this manuscript, by improving the classical iterative back projection (IBP) algorithm a super-resolution reconstruction algorithm is proposed. By fusing multiple quite noisy lowlight images having sub-pixel displacements between each other, both the spatial resolution and the SNR could be enhanced. In the in-lab experiments, the spatial resolution can be increased to nearly 1.8 times the original one. Besides that, the increment in SNR bigger than 6dB and 9dB could be obtained for the quarter moon and the new moon light condition respectively. The out-door experiments show the similar results and besides that by fusing sub-pixel shifted low-light images corresponding to different low-light conditions together, the reconstructed high-resolution images will have even better visual performance.
Medical ultrasound images are usually corrupted by the noise during their acquisition known as speckle. Speckle noise removal is a key stage in medical ultrasound image processing. Due to the ill-posed feature of image denoising, many regularization methods have been proved effective. This paper introduces an approach which collaborate both sparse dictionary learning and regularization method to remove the speckle noise. The method trains a redundant dictionary by an efficient dictionary learning algorithm, and then uses it in an image prior regularization model to obtain the recovered image. Experimental results demonstrate that the proposed model has enhanced performance both in despeckling and texture-preserving of medical ultrasound images compared to some popular methods.
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