In low-light-level detection, glow and hot pixels in some imaging sensors become visible due to long-exposure time, leading to image quality degradation. To solve the problem of glow and hot pixels in a single image, an improved extraction algorithm based on the idea of robust principal component analysis is proposed to remove them. The image is divided into three terms in our algorithm: a low-rank matrix (image without glow and hot pixels), an extremely sparse matrix (hot pixels), and a sparse and spatially smooth matrix (glow). Specifically, the total variation norm and ℓ₁-norm are exploited to describe the property of glow. Moreover, a top-hat filter and a boundary-searching method are introduced into the soft threshold operator to improve accuracy. The superiority of the proposed approach is demonstrated with evaluations on simulated datasets, quantitative metrics, and real data.