Algorithms for image fusion were evaluated as part of the development of an airborne Enhanced/Synthetic Vision System (ESVS) for helicopter Search and Rescue operations. The ESVS will be displayed on a high- resolution, wide field-of-view helmet-mounted display (HMD). The HMD full field-of-view (FOV) will consist of a synthetic image to support navigation and situational awareness, and an infrared image inset will be fused into the center of the FOV to provide real-world feedback and support flight operations at low altitudes. Three fusion algorithms were selected for evaluation against the ESVS requirements. In particular, algorithms were modified and tested against the unique problem of presenting a useful fusion of varying quality. A pixel averaging algorithm was selected as the simplest way to fuse two difference sources of imagery. Two other algorithms, originally developed for real- time fusion of low-light visible images with infrared images, (one at the TNO Human Factors Institute and the other at the MIT Lincoln Laboratory) were adapted and implemented. To evaluate the algorithms' performance, artificially generated infrared images were fused with synthetic images and viewed in a sequence corresponding to a search and rescue scenario for a descent to hover. Application of all three fusion algorithms improved the raw infrared image, but the MIT-based algorithm generated some undesirable effects such as contrast reversals. This algorithm was also computationally intensive and relatively difficult to tun. The pixel averaging problem was simplest in terms of per-pixel operations and provided good results. The TNO-based algorithm was superior in that while it was slightly more complex than pixel averaging, it demonstrated similar results, was more flexible, and had the advantage of predictably preserving certain synthetic features which could be used to support obstacle detection.