Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
This work addresses the problem of improving efficiency in HDR imaging for researchers and engineers, though it appears incremental as it adapts existing compressive sensing methods.
The paper tackles the problem of reconstructing signals and images from periodic nonlinearities by designing a measurement scheme that supports efficient reconstruction, with potential applications to reduce measurement complexity in high dynamic range (HDR) imaging systems while maintaining reconstruction quality, as demonstrated by numerical experiments on real data.
We consider the problem of reconstructing signals and images from periodic nonlinearities. For such problems, we design a measurement scheme that supports efficient reconstruction; moreover, our method can be adapted to extend to compressive sensing-based signal and image acquisition systems. Our techniques can be potentially useful for reducing the measurement complexity of high dynamic range (HDR) imaging systems, with little loss in reconstruction quality. Several numerical experiments on real data demonstrate the effectiveness of our approach.