## 1 Introduction
Nowadays, an increasing number of researchers have adopted the use of a Guided Filter [12; 13] to process the image for its simplicity and efficiency without the gradient reversal artifacts. [PERSON] et al. [14] presented an edge-preserving filter with locally adaptive property, which is particularly effective in preserving or enhancing local details. Besides, researchers also applied hat-top transform [15; 16] or wavelet transform [17] to decompose the image, then process the layers individually followed by composing them. Although multiscale decomposition methods have strength on details enhancing, they sometimes trigger halo artifacts in strong edges. Currently, based on the human visual system (HVS), Retinex theory [18; 19], researchers have presented various methods [20; 21; 22; 23; 24; 25; 26]. The critical challenge of those algorithms is the contradiction between calculation speed and the processing effect. On the one hand, better results can be obtained by constructing more complex models; on the other hand, the intricate structure of the model increases the calculation complexity, which limits the widespread application of the algorithm. Meanwhile, HVS-based approaches are more appropriate for the visual images with sufficient details rather than the infrared images lack of details in general. In conclusion, the inadequacies of the existing methods mainly include (1) the contradiction between the maintaining of local details and the global consistency of the entire image, (2) too many parameters need to be selected manually with experience, and (3) poor robustness for dim images lack of details. Nowadays, infrared and visible light images fusion [27; 28] and the fusion of multi-exposure images [29; 30] are both research hotspots in image processing. The visible light sensor mainly captures the reflected light so that the visible light image has abundant background information. In contrast,the infrared sensor can capture the thermal radiation emitted by the object, and it is less impacted by the dark condition or the dim weather. Therefore, the fusion of infrared and visible light images can guarantee more complex and detailed scene information. Similarly, each of multi-exposure images has its own unique details. If these details are well fused into one image, a high-quality image with multiple details can be produced. This paper presents a novel approach based on adaptive transform and image fusion to overcome the problems above and display HDR infrared images on LDR display equipment with appropriate contrast and clear and abundant detail information. Inspired by the idea of image fusion, we transform the original image into multiple brightness by gamma transformation followed by multiscaled guided filter enhancement to keep and enhance the details in the entire image. In order to simplify the selection of parameters, we adapt the energy of the gradient (EOG) to guide the transformation, and the entropy is utilized to guide the multiscaled guided filter enhancement. The experimental results can prove that our method can achieve acceptable results with the fixed parameters. For typical HDR infrared images of various scenes, the effect of our method is robust. The rest of this paper can be chiefly described as follows. Section 2 describes the fundamental theory and specific steps of our proposed method. In Section 3, our experiment comparison of the methods are described in detail. In Section 4, the conclusion of the paper is presented. Finally, the acknowledgment is made in Section 5.