Multi-exposure microscopic image fusion-based detail enhancement algorithm

Dynamic comprehensive learning-based dung beetle optimizer using triangular mutation for polyps image segmentation

The process of early diagnosis of polyps is considered a critical point of preventive healthcare, as it can significantly improve the prognosis and treatment outcomes of patients with colorectal cancer. Since the Polyps are constructed in the colon, they can grow up to be cancerous over time. We present an alternative Polyps image segmentation approach according to multilevel thresholding techniques (MLTs) to ensure effective early polyp diagnosis. The developed MLT Polyps image segmentation method depends on improving the performance of the Dung beetle optimizer (DBO) algorithm based on the operators of Triangular Mutation (TMO), Comprehensive learning (CL), and dynamic update of the search domain. We conducted a comprehensive evaluation of the performance of the DCTDBO using eight Polyps images and compared it with other image segmentation methods, ensuring a rigorous and thorough process. The results show the high ability of the DCTDBO according to performance measures to segment the Polyps images. In terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity (FSIM), DCTDBO performed better than the basic version of the DBO and other methods. For example, the average of DCTDBO overall threshold levels and tested images in terms of FSIM, SSIM, and PSNR is 0.9668, 0.99217, and 28.9338, respectively. This indicates the influence of TMO and CL on enhancing the performance of DBO.

Bayesian multi-exposure image fusion for robust high dynamic range ptychography

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. The expectation-maximization (EM) algorithm is employed to infer the model parameters. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.

Conditional Random Field-Guided Multi-Focus Image Fusion

Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important. Transform-domain methods have been applied to image fusion, however, they are likely to produce artifacts. In order to cope with these issues, we introduce the Conditional Random Field (CRF) CRF-Guided fusion method. A novel Edge Aware Centering method is proposed and employed to extract the low and high frequencies of the input images. The Independent Component Analysis—ICA transform is applied to high-frequency components and a Conditional Random Field (CRF) model is created from the low frequency and the transform coefficients. The CRF model is solved efficiently with the α-expansion method. The estimated labels are used to guide the fusion of the low-frequency components and the transform coefficients. Inverse ICA is then applied to the fused transform coefficients. Finally, the fused image is the addition of the fused low frequency and the fused high frequency. CRF-Guided fusion does not introduce artifacts during fusion and supports image denoising during fusion by applying transform domain coefficient shrinkage. Quantitative and qualitative evaluation demonstrate the superior performance of CRF-Guided fusion compared to state-of-the-art multi-focus image fusion methods.