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Supercritical lens can create a sub-diffraction-limited focal spot in the far field, providing a promising route for the realization of label-free super-resolution imaging through the point scanning mechanism. However, all of the reported supercritical lenses have circular shape configurations, and produce isotropic sub-diffraction-limited focal spots in the focal plane. Here, we propose and experientially demonstrate a sub-diffraction transverse optical needle by using an elliptical supercritical lens. Through breaking the circular symmetry and introducing ellipticity to the lens, a uniform sub-diffractive transverse optical needle with lateral length and width of 6λ/NA and 0.45λ/NA, respectively, was successfully created in the focal plane. Further, elliptical sector-shape cutting with an optimized apex angle of 60 degrees can lead to suppressed subsidiary focusing for improved uniformity and condensed field intensity of the transverse optical needle. The demonstration of sub-diffractive transverse optical needle with a high aspect ratio (length to width ratio) of 13:1 may find potential applications in line-scanning microscopy for video-rate label-free super-resolution imaging, and also enable advances in the fields from laser manufacturing to optical manipulation. [ABSTRACT FROM AUTHOR]

It is difficult to improve image resolution in hardware due to the limitations of technology and too high costs, but most application fields need high resolution images, so super-resolution technology has been produced. This paper mainly uses information redundancy to realize multi-frame super-resolution. In recent years, many researchers have proposed a variety of multi-frame super-resolution methods, but it is very difficult to preserve the image edge and texture details and remove the influence of noise effectively in practical applications. In this paper, a minimum variance method is proposed to select the low resolution images with appropriate quality quickly for super-resolution. The half-quadratic function is used as the loss function to minimize the observation error between the estimated high resolution image and low-resolution images. The function parameter is determined adaptively according to observation errors of each low-resolution image. The combination of a local structure tensor and Bilateral Total Variation (BTV) as image prior knowledge preserves the details of the image and suppresses the noise simultaneously. The experimental results on synthetic data and real data show that our proposed method can better preserve the details of the image than the existing methods. [ABSTRACT FROM AUTHOR]