Blind Deconvolution Based on Compressed Sensing with bi-l0-l2-norm Regularization in Light Microscopy Image

Abstract

Blind deconvolution of light microscopy images could improve the ability of distinguishing cell-level substances. In this study, we investigated the blind deconvolution framework for a light microscope image, which combines the benefits of bi-l0-l2-norm regularization with compressed sensing and conjugated gradient algorithms. Several existing regularization approaches were limited by staircase artifacts (or cartooned artifacts) and noise amplification. Thus, we implemented our strategy to overcome these problems using the bi-l0-l2-norm regularization proposed. It was investigated through simulations and experiments using optical microscopy images including the background noise. The sharpness was improved through the successful image restoration while minimizing the noise amplification. In addition, quantitative factors of the restored images, including the intensity profile, root-mean-square error (RMSE), edge preservation index (EPI), structural similarity index measure (SSIM), and normalized noise power spectrum, were improved compared to those of existing or comparative images. In particular, the results of using the proposed method showed RMSE, EPI, and SSIM values of approximately 0.12, 0.81, and 0.88 when compared with the reference. In addition, RMSE, EPI, and SSIM values in the restored image were proven to be improved by about 5.97, 1.26, and 1.61 times compared with the degraded image. Consequently, the proposed method is expected to be effective for image restoration and to reduce the cost of a high-performance light microscope.

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

“…These approaches derived improved results; however, unnatural images were generated, and staircase or cartooned artifacts occurred in the case of the l 2 -norm-based image prior [57,58]. The Bi-l 0 -l 2 -norm prior strategy is an example in which the improved resolution prevents artifacts; however, noise amplification cannot be suppressed [59]. Nasonov and Krylov [60] introduced an image restoration method using deblurring after BM3D denoising.…”

“…However, unnatural results such as cartooned artifacts are difficult to use in light microscopic image processing. We are continuously performing research and development activities to improve microscopic images [57,59] and implement the proposed method to address existing problems. Denoising was performed after separating the subbands where noise was mainly distributed through the MRA using NSCT, and information loss was minimized by obtaining high-resolution subband images without artifacts through l 1 -norm-based deblurring.…”

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

“…Optimization methods also find applications in microscopy, for example [129][130][131][132][133]. Their shortcomings are not so critical for this area, since the optical schemes correspond much more strictly to the concept of a linear spatially invariant system.…”