A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing


Over the last decade, a number of Computational Imaging (CI) systems have been proposed for tasks such as motion deblurring, defocus deblurring and multispectral imaging. These techniques increase the amount of light reaching the sensor via multiplexing and then undo the deleterious effects of multiplexing by appropriate reconstruction algorithms. Given the widespread appeal and the considerable enthusiasm generated by these techniques, a detailed performance analysis of the benefits conferred by this approach is important.

Unfortunately, a detailed analysis of CI has proven to be a challenging problem because performance depends equally on three components: (1) the optical multiplexing, (2) the noise characteristics of the sensor, and (3) the reconstruction algorithm which typically uses signal priors. A few recent papers have performed analysis taking multiplexing and noise characteristics into account. However, analysis of CI systems under state-of-the-art reconstruction algorithms, most of which exploit signal prior models, has proven to be unwieldy. In this paper, we present a comprehensive analysis framework incorporating all three components.

In order to perform this analysis, we model the signal priors using a Gaussian Mixture Model (GMM). A GMM prior confers two unique characteristics. Firstly, GMM satisfies the universal approximation property which says that any prior density function can be approximated to any fidelity using a GMM with appropriate number of mixtures. Secondly, a GMM prior lends itself to analytical tractability allowing us to derive simple expressions for the ‘minimum mean square error’ (MMSE) which we use as a metric to characterize the performance of CI systems. We use our framework to analyze several previously proposed CI techniques (focal sweep, flutter shutter, parabolic exposure, etc.), giving conclusive answer to the question: ‘How much performance gain is due to use of a signal prior and how much is due to multiplexing? Our analysis also clearly shows that multiplexing provides significant performance gains above and beyond the gains obtained due to use of signal priors.


  1. K. Mitra, O. Cossairt and A. Veeraraghavan, “A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing”, IEEE Transactions on Pattern Analysis and Machine Learning (TPAMI), 2014. PDF, PPT, Poster

  2. O. Cossairt, A. Veeraraghavan, K. Mitra and M. Gupta, “Performance Bounds for Computational Imaging”, Imaging and Applied Optics Technical Papers, OSA, 2013.

Related publications

  1. K. Mitra, O. Cossairt and A. Veeraraghavan, “Can we Beat Hadamard Multiplexing? Data-driven Design and Analysis for Computational Imaging Systems”, IEEE International Conference on Computational Photography (ICCP), 2014.

  2. K. Mitra, O. Cossairt and A. Veeraraghavan, “To Denoise or Deblur: Parameter Optimization for Imaging Systems”, SPIE Electronic Imaging, 2014.