This paper presents image enhancement for a novel Ultra-High-Definition (UHD) video camera offering 4K images and higher. Conventional image enhancement techniques need to be reconsidered for the high-resolution images and the low-light sensitivity of the new sensor. We study two image enhancement functions and evaluate and optimize the algorithms for embedded implementation in programmable logic (FPGA). The enhancement study involves high-quality Auto White Balancing (AWB) and Local Contrast Enhancement (LCE). We have compared multiple algorithms from literature, both with objective and subjective metrics. In order to objectively compare Local Contrast (LC), an existing LC metric is modified for LC measurement in UHD images. For AWB, we have found that color histogram stretching offers a subjective high image quality and it is among the algorithms with the lowest complexity, while giving only a small balancing error. We impose a color-to-color gain constraint, which improves robustness of low-light images. For local contrast enhancement, a combination of contrast preserving gamma and single-scale Retinex is selected. A modified bilateral filter is designed to prevent halo artifacts, while significantly reducing the complexity and simultaneously preserving quality. We show that by cascading contrast preserving gamma and single-scale Retinex, the visibility of details is improved towards the level appropriate for high-quality surveillance applications. The user is offered control over the amount of enhancement. Also, we discuss the mapping of those functions on a heterogeneous platform to come to an effective implementation while preserving quality and robustness.