Reduced-Dimensional Capture of High-Dynamic Range Images with Compressive Sensing

Shundao Xie, Wenfang Wu, Rongjun Chen and Hong-Zhou Tan

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-13

Abstract:

The range of light illumination in real scenes is very large, and ordinary cameras can only record a small part of this range, which is far lower than the range of human eyes’ perception of light. High-dynamic range (HDR) imaging technology that has appeared in recent years can record a wider range of illumination than the perceptual range of the human eye. However, the current mainstream HDR imaging technology is to capture multiple low-dynamic range (LDR) images of the same scene with different exposures and then merge them into one HDR image, which greatly increases the amount of data captured. The advent of single-pixel cameras (compressive imaging system) has proved the feasibility of obtaining and restoring image data based on compressive sensing. Therefore, this paper proposes a method for reduced-dimensional capture of high dynamic range images with compressive sensing, which includes algorithms for front end (capturing) and back end (processing). At the front end, the K-SVD dictionary is used to compressive sensing the input multiple-exposure image sequence, thereby reducing the amount of data transmitted to the back end. At the back end, the Orthogonal Matching Pursuit (OMP) algorithm is used to reconstruct the input multiple-exposure image sequence. A low-rank PatchMatch algorithm is proposed to merge the reconstructed image sequence to obtain an HDR image. Simulation results show that, under the premise of reducing the complexity of the front-end equipment and the amount of communication data between the front end and the back end, the overall system achieves a good balance between the amount of calculation and the quality of the HDR image obtained.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6703528

DOI: 10.1155/2020/6703528

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