Azimuth resolution improvement for spaceborne SAR images with quasi-non-overlapped Doppler bandwidth

The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar （SAR） ima-ges with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images should be coregistered, while phase biases induced by topography, atmospheric propagation delays and baseline measurement errors should be calibrated. However, the coregistration accuracy suffers from large Doppler decorrelation caused by the quasi-non-overlapped Doppler band-width. Furthermore, the method used to estimate phase biases from interferogram of azimuth prefiltered SAR image pairs wil fail when there is no overlapped spectrum. The fringe simulation and maximum sharpness optimization are adopted to deal with the problems. Accordingly, a novel algorithm to coherently synthesize SAR images is presented. The experiment with the Terra SAR X-band （TerraSAR-X） satel ite data validates the performance of the presented method.

Depth map resolution improvement for 3D range-intensity correlation laser imaging

This Letter proposes a high bit-depth coding method to improve depth map resolution and render it suitable to human-eye observation in 3D range-intensity correlation laser imaging. In this method, a high bit-depth CCD camera with a nanosecond-sealed gated intensifier is used as an image sensor; subsequently two high bit-depth gate images with specific range-intensity profiles are obtained to establish the gray depth map and finally the gray depth map is encoded by an equidensity pseudocolor. With this method, a color depth map is generated with higher range resolution. In our experimental work, the range resolution of the depth map is improved by a factor of 1.67.

Depth resolution improvement of streak tube imaging lidar system using three laser beams

The work proposes a three-laser-beam streak tube imaging lidar system. Besides the main measuring laser beam,the second beam is used to decrease the error of time synchronization. The third beam has n＋0.5 pixels＇ difference compared to the main measuring beam on a CCD, and it is used to correct the error caused by CCD discrete sampling. A three-dimensional（3D） imaging experiment using this scheme is carried out with time bin size of 0.066 ns（i.e., corresponding to a distance of 9.9 mm）. An image of a 3D model is obtained with the depth resolution of 〈2 mm, which corresponds to ~0.2 pixel.

Combination of super-resolution reconstruction and SGA-Net for marsh vegetation mapping using multi-resolution multispectral and hyperspectral images

Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.

Improvement of silicon etching resolution using the confined etchant layer technique

WHEN scanning electrochemical microscopy (SECM) with feedback mode is used to etchcertain surface, the etchant molecules generated at a microelectrode diffuse to the surface andreact therein with the surface species, resulting in local etching pattern. It is noted that theetching resolution of SECM is dominantly determined by the size of the microelectrode.However, many experimental results have shown the significant influence of the lateral diffu-sion of etchant on the etching resolution. Therefore, a thin diffusion layer of the

Coherent optical adaptive technique improves the spatial resolution of STED microscopy in thick samples

Stimulated emission depletion(STED) microscopy is one of far-field optical microscopy techniques that can provide sub-diffraction spatial resolution. The spatial resolution of the STED microscopy is determined by the specially engineered beam profile of the depletion beam and its power. However, the beam profile of the depletion beam may be distorted due to aberrations of optical systems and inhomogeneity of a specimen's optical properties, resulting in a compromised spatial resolution. The situation gets deteriorated when thick samples are imaged. In the worst case, the severe distortion of the depletion beam profile may cause complete loss of the superresolution effect no matter how much depletion power is applied to specimens. Previously several adaptive optics approaches have been explored to compensate aberrations of systems and specimens. However, it is difficult to correct the complicated high-order optical aberrations of specimens. In this report, we demonstrate that the complicated distorted wavefront from a thick phantom sample can be measured by using the coherent optical adaptive technique. The full correction can effectively maintain and improve spatial resolution in imaging thick samples.

Improving the resolution in quantum and classical temporal imaging

The point-spread function of an optical system determines its optical resolution for both spatial and temporal imaging. For spatial imaging, it is given by a Fourier transform of the pupil function of the system. For temporal imaging based on nonlinear optical processes, such as sum-frequency generation or four-wave mixing, the pointspread function is related to the waveform of the pump wave by a nonlinear transformation. We compare the point-spread functions of three temporal imaging schemes： sum-frequency generation, co-propagating four-wave mixing, and counter-propagating four-wave mixing, and demonstrate that the last scheme provides the best temporal resolution. Our results are valid for both quantum and classical temporal imaging.