On 4 September 2026 at 12.15 Shivasubramanian Gopinath will defend his doctoral thesis “Advanced multidimensional incoherent imaging systems with reconfigurable axial and spectral resolutions”.
Dr. Vijayakumar Anand ERA CIPHR Chair Holder and Associate Professor Institute of Physics, University of Tartu, Estonia
Opponent:
Dr. Enrique Tajahuerce Professor, Institute of New Imaging Techniques (INIT) Department of Physics, Jaume I University, Spain.
Summary
Modern imaging technologies such as cameras, microscopes, and telescopes are essential tools in both science and everyday life. They help us observe everything from tiny cells to distant galaxies. However, most current imaging systems have an important limitation: once an image, video, or hologram is recorded, its key properties, such as depth sharpness (axial resolution) and detail across the image (lateral resolution), cannot be altered. In addition, the axial and lateral resolutions are linked to each other. This means that changing one resolution often affects the other. Because of this, scientists and engineers must decide all image settings in advance. If different settings are needed later, the measurement must be repeated. In many real-life situations, this is difficult, expensive, or even impossible, for example, when studying rare events or living systems. This doctoral thesis introduces new computational imaging methods that solve this problem. The proposed approach allows key image properties, especially axial and spectral resolutions, to be adjusted after data recording without affecting lateral resolution. To achieve this, the research combines a specially designed optical system with advanced computational reconstruction algorithms. Together, they separate, or decouple, image properties that were previously interdependent. As a result, the same recorded data can be used in multiple ways, without repeating experiments or changing the imaging setup. This represents a significant shift from traditional imaging, where image properties are fixed at the moment of capture. Instead, images become flexible data that can be refined and adapted later. The potential applications are wide-ranging. In medical imaging, doctors could extract more useful information from existing scans without additional procedures. In microscopy, researchers could better study complex biological samples at different depths. In digital holography and computational photography, image analysis becomes more flexible and efficient. Even in cinematography, scenes that are slightly out of focus or improperly captured could be improved afterward, saving time and resources. Overall, this work presents a new way of thinking about imaging. Instead of fixed results, images become adaptable and reusable. This flexibility can support more efficient research, reduce costs, and enable smarter technologies in the future.
