A differentiable approximation approach to contrast-aware image fusion

Kenji Hara, Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    We propose a new weight optimization method for image fusion to obtain enhanced images. Given as input a set of images of a static scene captured under different photographic conditions such as exposure time and depth of focus, the algorithm modifies the input images based on visual saliency and then searches for a linear combination of the images that maximizes the total amount of gradient magnitudes. The search is performed by approximating a non-differentiable Lagrangian with the log-sum-exp function and then iteratively updating the closed-form analytical solution until convergence. The simple algorithm has converged fast and has demonstrated significant improvement in image quality over several conventional techniques.

    Original languageEnglish
    Article number6781588
    Pages (from-to)742-745
    Number of pages4
    JournalIEEE Signal Processing Letters
    Issue number6
    Publication statusPublished - Jun 2014

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics


    Dive into the research topics of 'A differentiable approximation approach to contrast-aware image fusion'. Together they form a unique fingerprint.

    Cite this