Infrared (IR) small target detection under complex environments is an essential part of infrared search and track systems. However, previously proposed infrared small target detection algorithms cannot achieve complete suppression of complex and significant backgrounds. The spatial-temporal information of image sequences is not fully exploited. In this paper, we present a sparse regularization-based twist tensor model for infrared small target detection. First, the twist tensor model is built via perspective conversion based on the target’s local continuity in the spatial-temporal domain, which makes the original complicated background components more structured and increases the difference between background and target. Then, the structured sparsity inducing norm is introduced to define the locality and continuity of the target. To further minimize the sparse background structures and global noise, the structured sparsity inducing norm and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm are combined as the target’s parse constraint. Experimental results on real scenes reveal that the suggested method can process images with high detection accuracy and outstanding background suppression ability compared to various state-of-the-art methods.