Image enhancement with intensity transformation on embedding space

In recent times, an image enhancement approach, which learns the global transformation function using deep neural networks, has gained attention. However, many existing methods based on this approach have a limitation: their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images. In order to address this limitation, a simple yet effective approach for image enhancement is proposed. The proposed algorithm based on the channel-wise intensity transformation is designed. However, this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours. To this end, the authors define the continuous intensity transformation (CIT) to describe the mapping between input and output intensities on the embedding space. Then, the enhancement network is developed, which produces multi-scale feature maps from input images, derives the set of transformation functions, and performs the CIT to obtain enhanced images. Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’ approach improves the performance of conventional intensity transforms on colour space metrics. Specifically, the authors achieved a 3.8% improvement in peak signal-to-noise ratio, a 1.8% improvement in structual similarity index measure, and a 27.5% improvement in learned perceptual image patch similarity. Also, the authors’ algorithm outperforms state-of-the-art alternatives on three image enhancement datasets: MIT-Adobe 5K, Low-Light, and Google HDR+.