Image Processing Reference
InDepth Information
Thus, the night color image enhancement can increase the scope of such algorithms by enhan
cing nightime images.
However, the night color image enhancement is a challenging task. Currently, the main
techniques for the night image enhancement are the image fusion and image enhancement.
Image fusion techniques include two categories: one is the fusion of the nightime image and
visible image [
1
,
2
]
and another is the fusion of the nightime image and infrared image [
3
,
4
].
These methods require multiple different spectral images collected in the same scene and have
high computational complexity. The main techniques for the image enhancement include con
trast stretching, slicing, histogram equalization, and some algorithms based on the retinex
[
5

11
], etc. Of all these algorithms, the algorithm based on the retinex has acceptable results,
but it will produce the “halo effect” and high time complexity.
In this article, we propose a novel algorithm for enhancing the night color image based on
the statistical law and the retinex. We assume that there is a transformation on the brightness
components of the pixel values between the nightime image and illumination image. There
fore, through this transformation, we can accurately and quickly get the illumination image.
Then, we can get the resulting image successfully based on the retinex. The resulting image
retains image details and exhibits higher brightness, so that the overall image looks more har
monious and natural. Our algorithm is simpler and faster compared to the other algorithms.
The rest of this article is organized as follows. In
Section 2
, overview of retinex theory is giv
lysis of the transformation law and enhancing the nightime image. Finally, the experimental
2 Overview of Retinex Theory
2.1 The Basic Idea of Retinex Theory
The Retinex theory deals with the removal of unfavorable illumination effects from a given
image. A commonly assumed model suggests that any given image
S
is the pixelwise mul
tiplication of two images, the reflection image
R
, and the illumination image
L
. This model is
given in the following equation:
(1)
Therefore, if we can get the illumination image, we can quickly get the reflection image. In
the actual calculation, a lookuptable log operation transfers this multiplication into an addi
tion, resulting with
s
= log(
S
) = log(
L
) + log(
R
) = l +
r
.
2.2 The “halo effect”
The Retinex algorithm often results in the “halo effect.” It is mainly based on the position of
surrounding pixels to give different weights to estimate the current pixel illumination in the
calculation of the illumination, while ignoring the pixel itself. This often leads to mutual inlu
ence between different pixels in the intense chiaroscuro edge region: the estimated illumina
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