Study Of Rapid Imaging Methods For Scanning Ion Conductance Microscopy

Scanning ion conductance microscopy(SICM)technology is a scanning probe microscopy imaging technology,which has the advantages of micro and nano imaging resolution,easy preparation of probes,and the unique advantage of in vivo cell imaging under physiological conditions,and can achieve the morphology of conductors,semiconductors and non-conductors,mechanical,electrical and other information,It is capable of imaging the morphology,mechanics,and electrical information of conductors,semiconductors,and nonconductors,and is therefore playing an important role in electrochemistry,life sciences,and materials science.However,in terms of imaging time resolution,the redundancy of the probe scan trajectory greatly limits its imaging efficiency,and the optimization of the scan trajectory is very limited by the characteristics of the sample morphology,which is difficult to achieve;in terms of the spatial resolution of SICM imaging,although high-speed scanning can improve the image spatial resolution per unit time,but this will generate more image noise,while low-speed scanning,although less image noise,but The spatial resolution of the corresponding image is lower.In summary,the shortcomings of both imaging speed and image quality of SICM limit its in situ imaging capability.Based on the existing SICM imaging system,the paper investigates the compression-aware algorithm,improves the segmented orthogonal matching tracking reconstruction algorithm,and proposes an image resolution enhancement algorithm to significantly improve the temporal resolution and spatial resolution of SICM imaging.The main research contents and findings of the paper are summarized as follows:(1)To address the problem that SICM probe trajectories are redundant and trajectory optimization is difficult to be compatible with samples of different scales,this paper proposes a compression-awareness-based Under-Sampling image reconstruction method,which breaks the conventional point-by-point full-sampling form of SICM,uses the theory of compressionawareness,sparse the existing scanned images using wavelet transform,and combines the random Gaussian measurement matrix to obtain the sampling feature matrix,realizing the under-sampling image reconstruction without Based on this,an improved reconstruction algorithm,the adaptive segmented orthogonal matching tracking algorithm(ASt OMP),is proposed and used to reconstruct the Under-Sampling sample morphology;experimental results show that at an Under-Sampling rate of 0.5,the image reconstruction accuracy(SSIM)is as high as 95% and the imaging time is reduced by The experimental results show that the compression-aware Under-Sampling method can effectively improve the temporal resolution of SICM imaging.(2)To address the problem that the threshold setting of the segmented orthogonal matching tracking algorithm(St OMP)is difficult to meet the dynamic selection of atoms leading to low image reconstruction accuracy,an improved reconstruction algorithm ASt OMP is proposed,which improves the reconstruction accuracy by considering the contribution of all atoms to the image content in each atom selection process.The reconstruction comparison experiments between the improved algorithm and other algorithms show that the reconstruction accuracy of the improved algorithm is improved by at least 3% compared with the original algorithm under different sparsity.(3)In response to the problem of SICM’s difficulty in improving both temporal and spatial resolution when imaging complex sample morphologies,the paper proposes an image resolution enhancement algorithm in high-speed scanning mode.The algorithm firstly preprocesses the reconstructed image and removes the noise generated during the high-speed scanning process by using the median filtering algorithm;secondly,the target edge of the image is extracted by using the Canny edge detection algorithm,and then the missing pixel value of the edge is filled by using the new edge-oriented interpolation algorithm(NEDI),and the non-edge part of the image is filled by bilinear interpolation,and then the image resolution is enhanced;finally,the peak Finally,peak signal-to-noise ratio(PSNR)and mean square error(MSE)metrics were used to analyze the articular cartilage surface imaging.Finally,the peak signal-to-noise ratio(PSNR)and the mean square error(MSE)were analyzed.The experimental results showed that the peak signal-to-noise ratio was improved by 7 d B and the MSE was less than 1 when compared with the conventional 2× resolution scanned image at a scan speed of 480 nm/ms.In summary,this thesis has systematically analyzed and improved the shortcomings in temporal resolution,imaging signal-to-noise ratio and spatial resolution of SICM imaging,and significantly improved the imaging efficiency and imaging quality of SICM by compressing the perceptual Under-Sampling reconstruction imaging method,improving the reconstruction algorithm and image resolution enhancement,which effectively promotes SICM in the fields of electrochemistry,life science and material science It has effectively promoted the wide application of SICM in the fields of electrochemistry,life science,and material science.