IRST (Infrared Search and Track) has been applied to many military or civil fields such as precise guidance, aerospace, early warning. As a key technique, small target detection based on infrared image plays an important role. However, infrared targets have their own characteristics, such as target size variation, which make the detection work quite difficult. In practical application, the target size may vary due to many reasons, such as optic angle of sensors, imaging distance, environment and so on. For conventional detection methods, it is difficult to detect such size-varying targets, especially when the backgrounds have strong clutters. This paper presents a novel method to detect size-varying infrared targets in a cluttered background. It is easy to find that the target region is salient in infrared images. It means that target region have a signature of discontinuity with its neighboring regions and concentrates in a relatively small region, which can be considered as a homogeneous compact region, and the background is consistent with its neighboring regions. Motivated by the saliency feature and gradient feature, we introduce minimum target intensity (MTI) to measure the dissimilarity between different scales, and use mean gradient to restrict the target scale in a reasonable range. They are integrated to be multiscale MTI filter. The proposed detection method is designed based on multiscale MTI filter. Firstly, salient region is got by morphological low-pass filtering, where the potential target exists in. Secondly, the candidate target regions are extracted by multiscale minimum target intensity filter, which can effectively give the optimal target size. At last, signal-to-clutter ratio (SCR) is used to segment targets, which is computed based on optimal scale of candidate targets. The experimental results indicate that the proposed method can achieve both higher detection precision and robustness in complex background.