Efficient sparsity-based inversion for photon-sieve spectral imagers with transform learning
| Autor: | Tunc Alkanat, Ulas Ramaci, Fatih Cagatay Akyon, Figen S. Oktem |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inversion (meteorology) 02 engineering and technology Inverse problem 01 natural sciences Spectral imaging 010309 optics Photon sieve Superposition principle Computer Science::Computer Vision and Pattern Recognition 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Computer vision Deconvolution Artificial intelligence Coordinate descent business |
| Zdroj: | GlobalSIP |
| Popis: | We develop an efficient and adaptive sparse reconstruction approach for the recovery of spectral images from the measurements of a photon-sieve spectral imager (PSSI). PSSI is a computational imaging technique that enables higher resolution than conventional spectral imagers. Each measurement in PSSI is a superposition of the blurred spectral images; hence, the inverse problem can be viewed as a type of multiframe deconvolution problem involving multiple objects. The transform learning-based approach reconstructs the spectral images from these superimposed measurements while simultaneously learning a sparsifying transform. This is performed using a block coordinate descent algorithm with efficient update steps. The performance is illustrated for a variety of measurement settings in solar spectral imaging. Compared to approaches with fixed sparsifying transforms, the approach is capable of efficiently reconstructing spectral images with improved reconstruction quality. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|