Citation: | Xi Jiaqi, Chen Xiaodong, Wang Yi, et al. Details preserved brightness equalization algorithm for non-uniform illumination images[J]. Opto-Electronic Engineering, 2019, 46(4): 180439. doi: 10.12086/oee.2019.180439 |
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Overview: Digital imaging is widely used in medical, surveillance, machine vision and other fields. Due to the limited light source during image acquisition, the captured image may have uneven illumination. The specific performance is low image local gray value, less dark area information and poor image visual effect, which affect subsequent judgment of image features.
In order to overcome the limitation of current image enhancement algorithms for non-uniform illumination images, a brightness equalization algorithm is proposed to preserve the detail information of low illumination region and normal illumination region at the same time. According to the Retinex theory, the gray value of any point in the image can be determined by the product of ambient illumination and reflectivity. Ambient illumination changes slowly with position, corresponding to the low frequency components of the image. Reflectivity depends on the object itself, reflecting the characteristics of the surface color and detail edges of the object. It contains the high frequency components of the image.
In this paper, an illumination filter for illumination estimation is designed by combining the adjacent frequency of the pixel with a conventional Gaussian filter. The basic idea of the illumination filter is that the influence of any neighborhood pixel in the image on the central pixel is not only related to the distance from the central pixel, but also related to the adjacent frequency of the values of the pixels on the image. It can effectively separate illumination information and reflection information with details. Then, the designed illumination compensator is used to process the illumination information which is mainly composed of low frequency components. The compensator uses illumination threshold to divide different illumination areas to compensate for low illumination brightness and adjust the overall illumination. The reflected and compensated illumination is combined to obtain an enhanced image with details retained.
Considering that in the process of image enhancement, the suppression effect of image brightness unevenness is closely related to the image information quantity, image quality, enhancement degree and naturalness maintenance effect, this paper uses four image evaluation factors (Entropy, PSNR, EME, and LOE) to objectively evaluate the image quality. The experimental results show that compared with the classical NPEA algorithm, the average peak signal to noise ratio of the image increases by 15.4%, the average enhancement degree increases by 245.0%, and the average brightness step difference decreases by 25.4%. The results of the proposed algorithm can maintain the details of different illumination areas while balancing the brightness, and obtain a better visual effect.
Proposed image enhancement framework
Line chart of win-noise point
Pseudo code of image enhancement algorithm
Results for image 1. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 2. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 3. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 4. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 5. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 6. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 7. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm
Results for image 8. (a) Original image; (b) Enhanced image of EBCE; (c) Enhanced image of ACA; (d) Enhanced image of NPEA; (e) Enhanced image of the proposed algorithm