Phase stretch transform for super-resolution localization microscopy

Abstract

Super-resolution localization microscopy has revolutionized the observation of living structures at the cellular scale, by achieving a spatial resolution that is improved by more than an order of magnitude compared to the diffraction limit. These methods localize single events from isolated sources in repeated cycles in order to achieve super-resolution. The requirement for sparse distribution of simultaneously activated sources in the field of view dictates the acquisition of thousands of frames in order to construct the full super-resolution image. As a result, these methods have slow temporal resolution which is a major limitation when investigating live-cell dynamics. In this paper we present the use of a phase stretch transform for high-density super-resolution localization microscopy. This is a nonlinear frequency dependent transform that emulates the propagation of light through a physical medium with a specific warped diffractive property and applies a 2D phase function to the image in the frequency domain. By choosing properly the transform parameters and the phase kernel profile, the point spread function of each emitter can be sharpened and narrowed. This enables the localization of overlapping emitters, thus allowing a higher density of activated emitters as well as shorter data collection acquisition rates. The method is validated by numerical simulations and by experimental data obtained using a microtubule sample.

Classification of Drones Using Edge-Enhanced Micro-Doppler Image Based on CNN

“…PST based edge detection technique has been utilized for SAR image processing and biomedical image processing. PST can also be used to achieve super-resolution [21].…”

An Image Enhancement Algorithm Based on Fractional‐Order Phase Stretch Transform and Relative Total Variation

“…Literature [ 7 ] used the minimum error method to automatically calculate the segmentation point of the piecewise linear grey-scale transformation, which improved the visual effect of the image. In [ 8 ], the improved histogram equalization method and the contrast enhancement algorithm were combined to improve the local information contrast enhancement of low illumination images. An adaptive smoothing and enhancement method for images was proposed in [ 9 ]; when filtering, the filter weights of the processed pixels are dynamically determined; after smoothing the inside of the image region, the edge of the region in the image is also sharpened and enhanced, which effectively solves the contradiction between image smoothing and enhancement.…”

Phase-Stretch Adaptive Gradient-Field Extractor (PAGE)

“…It also exhibits superior properties over conventional derivative operators, particularly in terms of feature enhancement in noisy low contrast images. These properties have been exploited to develop image processing tools for clinical needs such as a decision support system for radiologists to diagnose pneumothorax [15,16], for resolution enhancement in brain MRI images [17], single molecule imaging [18], and image segmentation [19].…”