Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and
  Denoising
Xinyi Bai Steffi Agino Priyanka Hsiao-Jung Tung Yuankai Wang
Due to the low accuracy of object detection and recognition in many
intelligent surveillance systems at nighttime, the quality of night images is
crucial. Compared with the corresponding daytime image, nighttime image is
characterized as low brightness, low contrast and high noise. In this paper, a
bio-inspired image enhancement algorithm is proposed to convert a low
illuminance image to a brighter and clear one. Different from existing
bio-inspired algorithm, the proposed method doesn't use any training sequences,
we depend on a novel chain of contrast enhancement and denoising algorithms
without using any forms of recursive functions. Our method can largely improve
the brightness and contrast of night images, besides, suppress noise. Then we
implement on real experiment, and simulation experiment to test our algorithms.
Both results show the advantages of proposed algorithm over contrast pair,
Meylan and Retinex.