# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 4 Experimental Results
### Application to Low-Light Image Enhancement
estimated the transmission using the dark channel prior and obtained the latent image by solving the degradation model in (3) by  
\[f_{inv}(x)=\frac{g_{inv}(x)-A}{t(x)}+A. \tag{11}\]  
In the proposed method, we estimated the atmospheric light using the proposed method described in Section 3. The transmission of the inverted low-light image is estimated by using the trained random forest with dark channel and local contrast features. To evaluate the performance of the low-light image enhancement, we synthesized the low-light image by the non-linear gamma correction using a set of test images as shown in Figure 13. The adjustment parameter \(\gamma\) was set to 2.5. Figure 14 compares the objective performance of the low-light image enhancement using the existing and proposed methods. The first and second columns respectively show a set of ideal and synthesized low-light images. As shown in Figure 14c, the resulting image of the histogram equalization (HE) shows both over-enhancement and color distortion, because the cumulative distribution function is drastically changed in the narrow region [26]. [PERSON] et al. presented the gamma correction-based image enhancement method using the weighted cumulative distribution function [27]. However, [PERSON]'s method generated brightness saturation in the bright region, as shown in Figure 14d. [PERSON]'s method provided a significantly enhanced result, but it could not avoid color distortion in the resulting image, as shown in Figure 14e [25]. On the other hand, the proposed method provided better results than most existing methods without brightness saturation and color distortion, as shown in Figure 14f. Table 4 shows the objective assessments using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) [28]. Figure 13: A set of test images used in the objective assessments of low-light image enhancement [29]: (**a–j**) are the test images of size 768 \(\times\) 512 and the low-light image is synthesized by adjusting the non-linear gamma correction. Figure 15 shows the resulting images of the low-light image enhancement using real low-light images. The HE shows the contrasting enhanced result, but it did not preserve the details in the bright region, as shown in Figure 15b [26]. [PERSON]'s method provided a more enhanced result than that of HE [27]. However, the resulting image could neither appropriately control the brightness saturation, nor successfully enhance the dark region, as shown in Figure 15c. In the same manner, [PERSON]'s method also provided the resulting image with brightness saturation, and it showed imbalanced amplification of the color components, as shown in Figure 15d [25]. On the other hand, the proposed method provided the resulting image with an increased dynamic range, preserving the details of the bright region without color distortion. Table 5 shows objective assessments using the autoregressive-based image sharpness metric (ARISM), which evaluates the sharpness ratio by analyzing the autoregressive image model without reference images [30]. We used the ARISM as a metric to evaluate the amount of detail in the bright and dark regions. As summarized in Table 5, Although the HE provided the best score, the proposed method provided better visual quality, as shown Figure 15.