RPCA Infrared Small Target Detection Based on Local Entropy Reference in Preprocessing

Based on the non-local similarity of images, the use of image block recombination to obtain low-rank block images is the basic method for applying robust principal component analysis (RPCA) for infrared small target detection involving single-frame image. This paper introduces the process of applying the RPCA algorithm in infrared small target detection involving single-frame images and analyzes the influence of various blocking methods under different image backgrounds. To address the difficulty of selecting the image block window and sliding step size under a complex background, a selection method based on the larger value of the minimum local entropy of the image block is proposed. The experimental results show that by calculating the block local entropy of the image, taking the larger value of the minimum local entropy as a reference, and selecting the RPCA algorithm preprocessing scheme, better results can be achieved in the detection of small targets in a single frame of infrared images. This addresses the lack of experience of engineering personnel with regard to the application of the RPCA algorithm.