Low-frequency background estimation and noise separation from high-frequency for background and noise subtraction

In fluorescence microscopy, background blur and noise are two main factors preventing the achievement of highsignal -to -noise ratio (SNR) imaging. Background blur primarily emanates from inherent factors including the spontaneous fluorescence of biological samples and out -of -focus backgrounds, while noise encompasses Gaussian and Poisson noise components. To achieve background blur subtraction and denoising simultaneously, a pioneering algorithm based on low -frequency background estimation and noise separation from high -frequency (LBNH-BNS) is presented, which effectively disentangles noise from the desired signal. Furthermore, it seamlessly integrates low -frequency features derived from background blur estimation, leading to the effective elimination of noise and background blur in wide -field fluorescence images. In comparisons with other state-of-the-art background removal algorithms, LBNH-BNS demonstrates significant advantages in key quantitative metrics such as peak signal-to-noise ratio (PSNR) and manifests substantial visual enhancements. LBNH-BNS holds immense potential for advancing the overall performance and quality of wide -field fluorescence imaging techniques. (c) 2023 Optica Publishing Group