Polarimetric imaging, leveraging measurements of polarimetric parameters that encode distinct physical properties, finds wide applications across diverse domains. However, some critical polarization information is highly sensitive to noise, and denoising polarimetric images while preserving polarization information remains a challenge. The development of denoising techniques for polarized images can be roughly divided into three stages: The first stage involves the direct application of traditional image denoising algorithms, such as spatial/transform domain filtering. The second stage involves specially designed methods for polarized images, using image prior models for noise removal, such as principal component analysis and K-singular value decomposition. In the third stage, benefiting from advances in deep learning, denoising methods tend to integrate polarization characteristics with deep learning models for noise suppression. The residual dense network, U-Net, and other effective models are appropriately modified and supervised/self-supervised trained to handle the denoising problem of regular/extensive polarimetric images. In this paper, we perform a comparative study of polarimetric image denoising methods. These methods are first classified as learning-based and traditional methods. Then, the motivations and principles of different types of denoising methods are analyzed. Finally, some potential challenges and directions for future research are pointed out.

Confocal microscopy, as an advanced imaging technique for increasing optical resolution and contrast, has diverse applications ranging from biomedical imaging to industrial detection. However, the focused energy on the samples would bleach fluorescent substances and damage illuminated tissues, which hinders the observation and presentation of natural processes in microscopic imaging. Here, we propose a photonic timestamped confocal microscopy (PT-Confocal) scheme to rebuild the image with limited photons per pixel. By reducing the optical flux to the single-photon level and timestamping these emission photons, we experimentally realize PT-Confocal with only the first 10 fluorescent photons. We achieve the high-quality reconstructed result by optimizing the limited photons with maximum-likelihood estimation, discrete wavelet transform, and a deep-learning algorithm. PT-Confocal treats signals as a stream of photons and utilizes timestamps carried by a small number of photons to reconstruct their spatial properties, demonstrating multi-channel and three-dimensional capacity in the majority of biological application scenarios. Our results open a new perspective in ultralow-flux confocal microscopy and pave the way for revealing inaccessible phenomena in delicate biological samples or dim life systems.