Infrared (IR) thermography camera became an essential tool for monitoring applications such as pedestrian detection and equipment monitoring. Most commonly used IR cameras are Long Wavelength Infrared (LWIR) cameras due to their suitable wavelength for environmental temperature. Even though the cost of LWIR cameras had been on a decline, the affordable ones only provided low-resolution images. Enhancement techniques that could be applied to visible images often failed to perform correctly on low-resolution LWIR images. Many attempts on thermal image enhancement had been on high-resolution images. Stereo calibration between visible cameras and LWIR cameras had recently been improved in term of accuracy and ease of use. Recent visible cameras and LWIR cameras are bundled into one device, giving the capability of simultaneously taking visible and LWIR images. However, few works take advantage of this camera systems. In this work, image enhancement framework for visible and LWIR camera systems is proposed. The proposed framework consists of two inter-connected modules: visible image enhancement module and LWIR image enhancement module. The enhancement technique that will be experimented is image stitching which serves two purposes: view expansion and super-resolution. The visible image enhancement module follows a regular workflow for image stitching. The intermediate results such as homography and seam carvings labels are passed to LWIR image enhancement module. The LWIR image enhancement module aligns LWIR images to visible images using stereo calibrations results and utilizes already computed homography from visible images to avoid feature extraction and matching on LWIR images. The framework is able to handle difference in image resolution between visible images and LWIR images by performing sparse pixel-to-pixel version of image alignment and image projection. Experiments show that the proposed framework leads to richer image stitching's results comparing to the results from an existing commercial software.