Sparse denoising and adaptive estimation enhances the resolution and contrast of fluorescence emission difference microscopy based on an array detector

Abstract

An array detector allows a resolution gain for confocal microscopy by combining images sensed by a set of photomultipliers tubes (or sub-detectors). Several methods have been proposed to reconstruct a high-resolution image by linearly combining sub-detector images, especially the fluorescence emission difference (FED) technique. To improve the resolution and contrast of FED microscopy based on an array detector, we propose to associate sparse denoising with spatial adaptive estimation. We show on both calibration slides and real data that our approach applied to the full stack of spatially reassigned detector signals, enables us to achieve a higher reconstruction performance in terms of resolution, image contrast, and noise reduction.

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

“…For instance, Willis et al [25] assumed that white Gaussian noise was added to the original image (artifact free) in the process of performing 3D image reconstruction in transmitted light bright-field microscopy, and Theodore et al [26] modeled and conducted research in the form of adding Gaussian noise while simulating the light intensity curve of charge-coupled device (CCD)-based light microscopy. In addition, previous studies for noise reduction [27][28][29] have investigated that the degrading process of conventional microscopy system using photomultiplier, CCD, and complementary metaloxide-semiconductor sensors can be described as Equation (1), and this study performed based on this assumption. The presence of noise in the image leads to challenges in the accurate estimation of PSF and noise amplification owing to similar high-pass filtering.…”