CHAPTER 3 PROPOSED FACE ENHANCEMENT…
3.4 Fusion
(a) (b)
Fig. 3.9 The skin map (a) and its corresponding distance map (b) with
brighter pixels representing smaller distance values.
3.4 Fusion
After obtaining Mdistance, Yskin and Ynon-skin, each pixel of the finial luminance Yfinal(x,y) is a composition of the Yskin and Ynon-skin with Mdistance. Therefore, there should be a weight map based on Mdistance on the interpolation process. It is straightforward that if the distance of a pixel is very small, this pixel value should be closer to Yskin. We use the power-law curve with 0.4 by mapping the narrow range of smaller distance values into a wider range of bigger weight values. This curve can make the composition in the boundary regions between skin and non-skin sharper without halo effects.
The combination equation with power-law function is expressed by
( )
where t is the threshold of dilation times defined in Section 3.3. Finally, we use the same method in Eq. (2.5) with and original luminance Y to reconstruct the color image. Fig. 3.10 illustrates an example of a weight map and a final result.
final
Y
(a) (b)
(c)
Fig. 3.10 The distance map (a) and its corresponding weight map (b) by
power-law function with brighter pixels representing smaller weight value.
The bottommost image (c) is the final result of our proposed face enhancement method.
.
CHAPTER 4
EXPERIMENTAL RESULTS
In this chapter, the experimental results implemented by our proposed non-face enhancement method and face enhancement method are given. In our database, there are about 150 photos including landscapes and portraitists with image resolution about 1280 × 960. These photos include: (1) overexposed and/or underexposed problems; (2) low-contrast problems; (3) normal images with good exposure light. We will give some experimental results and five comparisons with following techniques:
HE technique [3];
Picasa software [2];
Exposure correction (Battiato’s algorithm [9]);
Local gamma correction (Capra’s algorithm [8]);
Shadow correction (Safonov’s algorithm [10]).
These experimental results and comparisons are shown in Section 4.1 for non-face images and in Section 4.2 for face images.
4.1 Experimental results of non-face images
There are four experiment results and comparisons of non-face images shown in Fig 4.1 through Fig. 4.4. These four results include a normal image, a photo with shadow areas, an image with a backlight condition, a dark scene image. Finally, there are more experimental results shown in Fig. 4.5.
First, Fig. 4.1(a) shows an image with good exposure light and others are
the results treated by different techniques. Through the proposed examination function, the HE technique is suitably applied to this image directly without artifacts. The result of the HE technique is shown in Fig. 4.1(b) which is the same as the result of our proposed method in Fig. 4.1(g). Figs. 4.1(c) through (f) show the comparisons with Picasa, Battiato’s algorithm, Capra’s algorithm and Safonov’s algorithm. Fig. 4.1(d) shows a slight over-exposed look in the regions of lotus leaves. In Fig. 4.1(e), there is a suitable result in local regions but the global contrast is decreasing. The results in Figs. 4.1(c) and (f) are almost the same as the original image. By comparing all results, we can see that the result of our proposed method is more vivid and high contrast.
Second, there is an image with shadow areas given in Fig. 4.2(a), and others are the results treated by different techniques. Through the proposed examination function, the HE technique is suitably applied to this image directly. Therefore, we can see that the results in Fig. 4.2(b) applied by the HE technique and (g) applied by our method are the same. By comparing other results in Figs 4.2(c) through (f), we see that the result of our proposed method is both good at shadow areas and other areas.
Third, Fig. 4.3(a) illustrates an image with over-exposed and under-exposed regions simultaneously. Through the examination function, the HE technique is not suitably applied to this image. We can see that in Fig 4.3(b), although the dark area is enhanced clearly by applying the HE technique, there is an obvious false contour in the sky area. In Fig. 4.3(d) applied by Battiato’s algorithm, although the dark area is enhanced clearly, the details in the sky area are loss. The result of Picasa in Fig 4.3(c) is almost the same with the original image. Figs 4.3(e) and (f) applied by Capra’s
method and Safonov’s method have slight improvement in shadow areas, and the global contrast of the results are reducing. In Fig 4.3 (g), our result not only has obvious improvement in shadow areas, but also keeps the details in the sky area.
Fourth, there is a dark scene in Fig. 4.4(a). The result in Fig. 4.4(b) applied by the HE technique shows that background noises have been amplified and there is a halo-effect in the light area. In Fig. 4.4(d) applied by Battiato’s method, there is also an obvious halo-effect in the light area. In Figs.
4.4(c) and (f) treated by Picasa and Safonov’s method, it can be seen that there are few differences between the original images and these two images.
The results of Capra’s method and our proposed method in Figs. 4.4(e) and (g) have obvious improvement in detail of the dark area without artifacts. Nothing that Capra’s method is based on pixel-by-pixel gamma correction with a non-linear masking, so it is a high complexity algorithm. By comparing the complexity, our proposed algorithm is accomplished by a modified global HE technique, so our proposed method can execute efficiently and automatically.
As illustrations of the experimental results, it should be pointed out that our proposed examination function can detect effectively if the HE technique is suitably applied to an image. If it is not qualified, our adjustment approach can produce well results without artifacts. Furthermore, there are more experimental results by our proposed method shown in Fig. 4.5.
(a) Original image (b) HE
(c) Picasa software (d) Battiato’s algorithm
(e) Capra’s algorithm (f) Safonov’ algorithm
(g) Our method
Fig. 4.1 A normal photo enhanced by difference techniques.
(a) Original image (b) HE
(c) Picasa software (d) Battiato’s algorithm
(e) Capra’s algorithm (f) Safonov’ algorithm
(g) Our method
Fig. 4.2 A photo with shadow areas enhanced by different techniques.
(a) Original image (b) HE
(c) Picasa software (d) Battiato’s algorithm
(e) Capra’s algorithm (f) Safonov’ algorithm
(g) Our method
Fig. 4.3 An image with a backlight condition enhanced by different methods.
(a) Original image (b) HE
(c) Picasa software (d) Battiato’s algorithm
(e) Capra’s algorithm (f) Safonov’ algorithm
(g) Our method
Fig. 4.4 A dark scene image enhanced by different methods.
Fig. 4.5 The original images in the left column and with their corresponding
results treated by our proposed method in the right column.
4.2 Experimental results of face images
In this section, we will show the experimental results and comparisons of two kinds of face images including a dark scene and a backlight condition shown in Fig. 4.6 and Fig. 4.7 respectively. Finally, there are more experimental results shown in Fig. 4.8.
First, Fig. 4.6(a) illustrates the face photo taken in the dark scene. The result of HE in Fig. 4.6(b) shows the noises are amplified and the face regions are over-exposed. The results applied by Picasa and Battiato’s method in Figs.
4.6(c) and (d) have improvement in skin regions, but the background is still unclear. The result applied by Capra’s method in Fig. 4.6(e) has more distinguishable details in the background, but the face regions seem unnatural influenced by insufficient contrast. Fig. 4.6(f) shows the slight improvement in both background and skin regions by Safonov’s method. By comparing all results, the result of our proposed method in Fig. 4.6(g) shows that there is not only a clearer background, but also satisfied illumination in skin regions.
Second, Fig. 4.7(a) illustrates a face photo taken in a backlight condition.
In Fig. 4.6(b), HE still produces a wash-out appearance in skin regions. The results of Figs. 4.6(c), (e) and (f) treated by Picasa, Capra’s method and Safonov’s method still have unsatisfied illumination in face regions. In Figs.
4.6(b), the face regions are both seems unnatural influenced by insufficient contrast in skin regions. Fig. 4.6(d) has satisfied illumination in face regions, but the details in the background are loss. The result of our method in Fig.
4.6(g) has not only appropriate illumination in face regions, but also a suitable background.
(a) Original image (b) HE
(c) Picasa software (d) Battiato’s algorithm
(e) Capra’s algorithm (f) Safonov’s algorithm
(g) Our method
Fig. 4.6 A face image in a dark scene enhanced by different techniques.
(a) Original image (b) HE
(c) Picasa software (d) Battiato’s algorithm
(e) Capra’s algorithm (f) Safonov’s algorithm
(g) Our method
Fig. 4.7 A face image with a backlight condition enhanced by different
techniques.
Fig.4.8 The original images in the left column and with their corresponding results treated by our proposed method in the right column.
CHAPTER 5
CONSLUSION AND FUTURE WORK
In this thesis, we have proposed an automatic and efficient non-face enhancement method and face enhancement method respectively. In the non-face enhancement method, we propose a contrast-stretching constraint based on JND to judge if the HE technique is suitably applied to an image. If it is not qualified, we present an adjustment approach to modify the histogram curve without destroying the monotonic property. In the face enhancement method, we improve our framework by combing our proposed non-face enhancement method and exposure correction technique provided by Battiato et al. [9] to obtain satisfied contrast in the background and appropriate illumination in skin regions. On this combining process, a distance map estimated by iterative morphological operations is used for this fusion purpose.
Experimental results show that our proposed method can produce suitable results without artifacts.
It can be noted that our proposed image enhancement method is based on the information of illumination, not colors. Therefore, it may be an interesting approach to extend our concept by combing color HE techniques [18] and the JND model.
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