Robust total variation-based destriping model via sparse representation learning for business infrared imaging systems

Abstract

Infrared images are often degraded by the issue of random and stripe noises in the business infrared focal plane imaging process. In this study, we proposed a novel infrared image destriping model with unidirectional and bidirectional total variation regularization. From the destriped infrared images, the stripe noise only exists in unidirection. The nature of unidirectional noise is leveraged to converted the total variation regularization and constructed the proposed model. For the random noise, the bidirectional total variation is introduced, which can preserve the image edge and texture details. Furthermore, the alternating direction method of multiplier algorithm is introduced to solve the infrared image destriping model. The proposed model is executed on several public infrared image datasets, thus outperforming the traditional destriping models in the qualitative evaluation index.