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We will present our paper 'Differentiable Uncalibrated Imaging' where we propose a differentiable imaging framework to address uncertainty in measurement coordinates, such as sensor locations and projection angles. Our approach leverages implicit neural networks and spline-based neural fields to perform joint calibration and image reconstruction. This makes it possible, for example, to use pre-trained neural networks to configurations different from those for which they were originally trained. We will begin by presenting the general optimization framework underlying our method.
Lensless cameras possess significant advantages when compared to lens-based ones. By replacing the lens with a coded aperture, the camera device can be made lighter, smaller, amenable towards compressive sensing techniques, and can be designed with less physical constraints. We leverage this freedom of design to propose a novel optimized coded mask that effectively extends the depth of field of a lensless camera. In this webinar, we will start by introducing the concept of lensless imaging along with the reason as to why general lensless cameras have a limited depth of field.
Fractional programming (FP) refers to optimization problems involving functions of ratios. FP plays an important role in signal processing and machine learning, because many problems in these application areas are fractionally structured. This talk focuses on a state-of-the-art FP method called the quadratic transform and illustrates the use of quadratic transform for discrete optimization problems through two application examples.
Section: Our Story
McMaster University,Ontario, Canada
Section: Our Story
University of Genova,Genoa, Italy
Section: Our Story
University of Alabama,Alabama, USA
These members have established themselves as leaders in their respective IEEE-designated fields of interest.
Section: Our Story
Investing in the Next Generation of Signal Processing Engineers
Corresponding Member: Marc Moonen (12/31/27)
Representative: Tulay Adali (12/31/26)
