Fluorescence Microscopy Denoizing via Neighbor Linear Embedding

One of the di ffi culties in studying fl uorescence imaging of biological structures is the presence of noise corruption. Even though hardware-and software-related technologies have undergone continual improvement, the unavoidable e ff ect of Poisson–Gaussian mixture type is generally encountered in fl uorescence microscopy images. This noise should be mitigated to allow the extraction of valuable information from fl uorescence images for various types of biological analysis. Thus, this study introduces a new and e ffi cient learning-based denoizing approach for fl uorescence microscopy. The proposed approach is based mainly on linear transformations between noise-free and noisy submanifold structures of patch spaces, bene fi ting from linear neighbor embeddings of local image patches. According to visual and statistical results, the developed algorithm called "neighbor linear-embedding denoizing" algorithm has a highly competitive and generally superior performance in comparison with the other algorithms used for fl uorescence microscopy image denoizing in the literature