Thermal infrared bands contain important information for various applications. Their spatial resolution is relatively low, and it is hard to determining the location of the targets in these bands. The aim of this study is to enhance spatial resolution of thermal bands. Image fusion is one of the efficient methods that are employed to enhance spatial resolution of thermal bands by fusing these data with high spatial resolution visible bands. Image fusion aims to integrate images with different spatial and spectral resolution, such that the synthesized image is more suitable for human visual perception or further processing, such as image classification, segmentation, texture feature extraction, object recognition, etc. We chose pixel level image fusion and added details and spatial information taken from the visible band to the thermal infrared band. Multi-resolution analysis (e.g. wavelet, laplacian pyramid, contourlet, curvelet, etc) is an effective pixel level fusion approach. In this paper, the contourlet transform in image fusion due to its advantages, high directionality and anisotropy is used. Because of the downsampling and upsampling, the contourlet transform lacks of shift invariance and results in artifacts. Therefore, we use the other kinds of contourlet transform, nonsubsampled and sharp frequency localization contourlet transform, and then, the image fusion performance of six multi-resolution transforms,  including the discrete wavelet, stationary wavelet, laplacian pyramid, contourlet, nonsubsampled and sharp frequency localization contourlet transform are compared. The methods have been tested using thermal infrared and visible landsat-8 data. The spectral and spatial quality assessment parameters (e.g. CC, SAM, ERGAS, SNR, RMSE, AG, UIQI, etc) show that the sharp frequency localization and the nonsubsampled contourlet transforms perform better than the discrete wavelet and the original contourlet transforms in terms of preserving radiance spectral information and increasing spatial details The experimental results show that the discrete wavelet and the contourlet transform are the worse than the other transformations. Therefore, the shift invariant property is of great importance for image fusion. The results of our comparative analysis show that in spite of the lack of a spectral overlap between the visible and the thermal infrared bands, the final fused thermal image keep its spectral characteristics while the spatial resolution is enhanced. It can be concluded that the sharp frequency localization contourlet transform with redundancy factor of 2.33 is the best technique for fusion of the visible and the thermal infrared bands. Finally, the effect of decomposition levels, on fusion performance result by sharp frequency localization contourlet transform with redundancy factor of 2.33 was investigated. It also can be concluded the appropriate setting for the number of decomposition levels is four.