ABSTRACT
We address the problem of limited temporal resolution in optical-resolution microscopy (OR-PAM) for cellular imaging by undersampling and reconstruction. A curvelet transform method in a compressed sensing framework (CS-CVT) was devised to specifically reconstruct the boundary and separability of cell objects in an image. The performance of the CS-CVT approach was justified by comparisons with the natural neighbor interpolation (NNI) followed by smoothing filters on various imaging objects. In addition, a full-raster scanned image was provided as a reference. In terms of structure, CS-CVT produces cellular images with a smoother boundary but less aberration. We found the strength of CS-CVT in recovering high frequency that is important in representing sharp edges which often missing in typical smoothing filters. In a noisy environment, CS-CVT was less affected by the noise compared to NNI with a smoothing filter. Furthermore, CS-CVT could attenuate noise beyond the full raster scanned image. By considering the finest structure in the cellular image, CS-CVT was performing well with a minimum range of undersampling around 5% to 15%. In practice, this undersampling was easily translated into 8- to 4-fold faster OR-PAM imaging. In summary, our approach improves the temporal resolution of OR-PAM without a significant tradeoff in image quality.
