Contrast enhancement method in aero thermal radiation images based on cyclic multi-scale illumination self-similarity and gradient perception regularization

Abstract

In aerospace, the effects of thermal radiation severely affect the imaging quality of infrared (IR) detectors, which blur the scene information. Existing methods can effectively remove the intensity bias caused by the thermal radiation effect, but they have limitations in the ability of enhancing contrast and correcting local dense intensity or global dense intensity. To address the limitations, we propose a contrast enhancement method based on cyclic multi-scale illumination self-similarity and gradient perception regularization solver (CMIS-GPR). First, we conceive to correct for intensity bias by amplifying gradient. Specifically, we propose a gradient perception regularization (GPR) solver to correct intensity bias by directly decomposing degraded image into a pair of high contrast images, which do not contain intensity bias and exhibit inverted intensity directions. However, we find that the GPR fails for dense intensity area due to small gradient of the scene. Second, to cope with the cases of dense intensity, we regard the dense intensity bias as the sum of multiple slight intensity bias. Then, we construct a cyclic multi-scale illumination self-similarity (CMIS) model by using multi-scale Gaussian filters and structural similarity prior to removing the dense intensity layer by layer. The result acts as coarse correction for GPR, which does not need to be overly concerned with whether the result has intensity residuals or not. Finally, the coarse corrected result is input to the GPR module to further correct residual intensity bias by enhancing contrast. Extensive experiments in real and simulated data have demonstrated the superiority of the proposed method.