All about images, toward the essence
We explore all aspects of images, including acquisition, analysis, modeling, expression, and interpretation—striving to uncover their fundamental nature and principles.
The word “image” often refers to a digital photograph, typically represented in RGB format. However, in a broader sense, an image can be any pattern that encodes the features or state of an object or phenomenon. In this sense, an image becomes a form of “representation” in both the scientific and mathematical sense. RGB images, for example, are just one way of capturing how objects appear to the human visual system. Other examples include depth images, spectral images, CT (computed tomography) images, and MRI (magnetic resonance imaging) images, each revealing different aspects of the target. Importantly, in mathematics, an “image” refers to the result of mapping a set or structure from one space to another via a function. This idea is fundamental to Image Informatics, where we regard images as structures that emerge from mapping real-world objects or phenomena—through sensors and computational processes—into new descriptive spaces such as pixels, waveforms, point clouds, or abstract feature vectors.
The Image Informatics Laboratory conducts interdisciplinary research into the fundamental structures, transformations, and expressive methods of such “patterns.” Our research extends beyond visual images to include sensor signals, shapes, motions, and time series, comprehensively addressing challenges from acquisition and analysis to interpretation and presentation.
1. Computational Imaging
An image is not the object itself, but a modality-dependent description—information about one aspect of a target as captured under specific measurement or observation conditions. For example, an RGB image records the intensity of reflected light from a specific viewpoint and under particular illumination. Changing the viewpoint or the measurement modality yields a different image. Every imaging process involves some degree of information loss. However, by acquiring data from multiple modalities and under diverse observation conditions, and integrating them using computational methods, it becomes possible to recover essential, invariant patterns and structures. This process can be viewed as twofold: encoding (acquisition) and decoding (integration and interpretation), involving optics, electronics, sparse modeling, deep learning, optimization, and other diverse technologies.
Our laboratory advances computational imaging and the integration of multimodal information across the full imaging pipeline—from designing novel imaging systems and algorithms to developing methods for reconstructing, enhancing, and interpreting complex image data. We aim to extract meaningful patterns from diverse sources, including medical scans, satellite images, 3D sensor data, and time-series measurements.
2. Understanding Motion and Deformation
Capturing the temporal changes of images is fundamental for understanding and predicting objects and phenomena. For example, the skeleton constrains human motion, and temporal patterns in lung CT scans, pathology slides, or cloud movement in weather prediction all reflect underlying physical and structural constraints.
We model such time-dependent deformations and motions using geometric and statistical methods. By formulating deformations as geometric transformations, we can enforce constraints such as isometry, conformality, or smoothness, enabling the analysis and reconstruction of complex, nonlinear changes. This unified framework supports the analysis of diverse and dynamic “images” in the real world, from living bodies to natural phenomena.
3. Visualization and Expression
Effective information visualization and expression require careful design of perspective—how to select and present information in ways that are most intuitive and meaningful. Not all information needs to be shown at once; instead, the goal is to choose and present what is most relevant as an “image” for a specific purpose.
Our laboratory advances the development of innovative visualization and presentation techniques, taking into account human perception and interaction. We go beyond conventional 2D displays, exploring a range of output modalities—including 3D displays, 3D printing, and other innovative devices—to expand how images are delivered and understood.