Infrared small target detection plays a crucial role in maritime security. However, detecting small targets within heavy sea clutter environments remains challenging. Existing methods often fail to deliver satisfactory performance in the presence of substantial clutter interference. This paper analyzes the spatial–temporal appearance characteristics of small targets and sea clutter. Based on this analysis, we propose a novel detection method based on the appearance stable isotropy measure (ASIM). First, the original images are processed using the Top-Hat transformation to obtain the salient regions. Next, a preliminary threshold operation is employed to extract the candidate targets from these salient regions, forming a candidate target array image. Third, to distinguish between small targets and sea clutter, we introduce two characteristics: the gradient histogram equalization measure (GHEM) and the local optical flow consistency measure (LOFCM). GHEM evaluates the isotropy of the candidate targets by examining their gradient histogram equalization, while LOFCM assesses their appearance stability based on local optical flow consistency. To effectively combine the complementary information provided by GHEM and LOFCM, we propose ASIM as a fusion characteristic, which can effectively enhance the real target. Finally, a threshold operation is applied to determine the final targets. Experimental results demonstrate that our proposed method exhibits superior comprehensive performance compared to baseline methods.

Infrared images play a crucial role in military reconnaissance, security monitoring, fire detection, and other tasks. However, due to the physical limitations of detectors, an infrared image often suffers from significant stripe noise. The presence of stripe noise significantly degrades image quality and subsequent processing, making the removal of such noise indispensable. In this study, we propose, to our knowledge, a novel low-rank decomposition model to separate the stripe noise components in infrared images. In comparison with existing algorithms for removing infrared stripe noise, our method takes into account the distinctiveness between stripe noise and information components. For the stripe noise component, we describe a column gradient domain low-rank prior and standard deviation weighted group sparsity prior. For the image information component, we employ a structure-aware gradient sparsity prior to suppress stripes while preserving the structural features of images. During the iterative solution process, we utilize both an initial solution based on minimizing column differences and an iteration step-size strategy based on variable acceleration to accelerate convergence. To validate the effectiveness of our proposed method, we conduct experiments to compare it with other destriping algorithms, demonstrating the superiority of our method from the perspectives of both subjective evaluation and objective metrics.