Night color image enhancement via statistical law and retinex - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 207

FIGURE 5 Scene 3. (a) original image; (b) enhanced image by Michael Elad; (c) enhanced
image by MSRCR; (d) enhanced image by the proposed algorithm.
FIGURE 6 Scene 4. (a) original image; (b) enhanced image by Michael Elad; (c) enhanced
image by MSRCR; (d) enhanced image by the proposed algorithm.
FIGURE 7 Scene 5. (a) original image; (b) enhanced image by Michael Elad; (c) enhanced
image by MSRCR; (d) enhanced image by the proposed algorithm.
FIGURE 8 Scene 6. (a) original image; (b) enhanced image by Michael Elad; (c) enhanced
image by MSRCR; (d) enhanced image by the proposed algorithm.
It can be clearly seen from the figures that the three algorithms all have a certain enhance-
ment efect on the nightime images. The processing results of MSRCR algorithm are too
bright leading to atomization phenomenon and loss of details, as shown in Figures 4(c) , 5(c) ,
6(c) , and 8(c) . The processing results of Michael Elad's algorithm tend to produce excessive
sharpening phenomenon in highlighting edges and the “halo artifacts” phenomenon which is
shown in Figures 6(b) , 7(b) , and 8(b) . Moreover, it often leads to noisy amplification in dark
areas, as shown in Figure 3(b) , which is mainly due to the unsmooth curve (shown in Figure
1 ) between source images and illumination images. Compared with the two algorithms, the