Infrared and Visible Image Fusion Under Different Illumination Conditions Based on Illumination Effective Region Map

It is well known that the quality of visible images taken under different illumination conditions varies greatly, so the illumination factors will affect infrared and visible image fusion effects inevitably. This paper proposes an infrared and visible image fusion algorithm that satisfies poor illumination conditions. The algorithm is divided into three stages: image preprocessing, multiscale decomposition and image fusion. The purpose of image preprocessing is to improve the contrast of visible image and extract the visual salient regions of infrared image. In the multiscale analysis stage, infrared and visible images are decomposed into different scales by combining Gaussian transform and Rolling Guidance Filter, which effectively avoids the halo artifacts. In the image fusion stage, the fusion weights of base layer coefficients are determined based on the combination of the saliency map of infrared image and the Illumination Effective Region Map of visible image. The detail layer coefficients of images are fused by choose-max fusion rule based on the local variance of detail features of the original images. Experiments show that the fusion effects of the proposed algorithm are robust to illumination variations, and the fused images have good detail clarity and can preserve the effective information of the original images well in an unsatisfactory illumination condition.