LAUSR

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 cells object in an image. The performance of 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 reference. In terms of structure, CS-CVT produces cellular images with 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 filter. In a noisy environment, CS-CVT was less affected by the noise compared to NNI with 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 minimum range of undersampling around 5% to 15%. In practice, this undersampling were easily translates into 8- to 4-fold faster OR-PAM imaging. In summary, our approach improves temporal resolution of OR-PAM without significant trade-off in image quality.