His research interests include system-level design methodology and design methodologies for cyberphysical systems. Taniguchi received his Ph.D. degree in information science and technology from Osaka University. He is a Member of IEEE and a member of the Association for Computing Machinery; Institute of Electronics, Information, and Communication Engineers; and

Recently, deep-learning-based low-light image enhancement (LLIE) methods achieved promising results with large collections of data. However, low-/normal-light image pairs are difficult to obtain in practice. Although the indoor training pairs may be synthesized by manually adjusting the light exposure, there is currently no outdoor LLIE benchmark captured in the real world. To bridge the gap and benchmark outdoor LLIE tasks, we propose the first outdoor, real-world, 2-D, low-light dataset, dubbed RE2L. Based on RE2L, we propose a semisupervised LLIE framework to further exploit the illumination knowledge from both the signal fidelity constraint and characteristics of normal-light natural images. As the images in our RE2L dataset have various illumination conditions, we propose integrating the illumination degree information into the generator as guidance for controllable LLIE. Experimental results demonstrate that using RE2L for training deep LLIE schemes can improve the model effectiveness, both quantitatively and visually, especially on semisupervised LLIE.

Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Secondly, we propose to explore cross-scale holistic contextual connections between every position pair of high-level feature maps regardless of their distances across different scales. It selectively aggregates features at each position based on its connections to all the others, simulating the “contrast” stimulus of visual saliency. Finally, we present a complementary features integration module to fuse low- and high-level features according to their properties. Experimental results demonstrate our proposed method outperforms previous state-of-the-art methods on the benchmark datasets, with the fast inference speed of 30 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPS</i> on a single GPU.

Bell Labs Fellow. He is also Convenor of the ISO/IEC JTC1/SC29/ WG2 on MPEG Technical Requirements. His current interests include immersive media services and standardization of immersive media communication protocols in MPEG and 3GPP. Curcio received the Ph.D. degree in signal processing from Tampere University of Technology. He is the Senior Member of the IEEE.