# Learning Weighted Forest and Similar Structure for Image Super Resolution
## 5 Experimental Results
### Visual Quality  
Just like many other SR works, our method also uses a single luminance channel because the high-frequency chrominance changes are less sensitive than luminance changes for human visual systems. The color image is first converted from the RGB space into a YCbCr space, and then an HR output image is restored only by luminance component and directly using bi-cubic interpolation for chrominance components. To prove the effectiveness of our algorithm, visual quality of the SR-reconstructed results of three typical test images (monarch, zebra and airplane) with up-sampling factor of 3, is displayed in Figures 1-3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Benchmark** & **Bi-cubic** & **NE+LLE** & **Zeyde** & **GR** & **ANR** & **RFL** & **SRCNN** & **Proposed** \\ \hline \multirow{2}{*}{Set5} & \(\times\) 3 & 30.39 & 31.84 & 31.90 & 31.41 & 31.92 & 32.46 & 32.39 & 32.73 \\  & \(\times\) 4 & 28.42 & 29.61 & 29.69 & 29.34 & 29.69 & 30.15 & 30.09 & 30.45 \\ \hline \multirow{2}{*}{Set14} & \(\times\) 3 & 27.54 & 28.60 & 28.67 & 28.31 & 28.65 & 29.05 & 29.00 & 29.41 \\  & \(\times\) 4 & 26.00 & 26.81 & 26.88 & 26.60 & 26.85 & 27.24 & 27.20 & 27.62 \\ \hline \multirow{2}{*}{B100} & \(\times\) 3 & 27.15 & 27.85 & 27.87 & 27.70 & 27.89 & 28.39 & 28.10 & 28.47 \\  & \(\times\) 4 & 25.92 & 26.47 & 26.51 & 26.37 & 26.51 & 26.86 & 26.66 & 26.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average PSNR Results for Comparison Methods. Figure 1: Visual quality comparison of a monarch image: (**a**) Original; (**b**) Bi-cubic; (**c**) Zeyde; (**d**) GR; (**e**) ANR; (**f**) NE+LLE; (**g**) SRCNN; (**h**) RFL; (**i**) Proposed. Figure 3: Visual quality comparison of an airplane image: (**a**) Original; (**b**) Bi-cubic; (**c**) Zeyde; (**d**) GR; (**e**) ANR; (**f**) NE+LE; (**g**) SRCNN; (**h**) RFL; (**i**) Proposed. Figure 2: Visual quality comparison of a zebra image: (**a**) Original; (**b**) Bi-cubic; (**c**) Zeyde; (**d**) GR; (**e**) ANR; (**f**) NE+LE; (**g**) SRCNN; (**h**) RFL; (**i**) Proposed. As can be seen from the figures above, the bi-cubic interpolation usually generates obviously blurring artifacts around the edges and produces unpleasing visual effects in the texture and text regions. [PERSON]'s method implements SR based on sparse coding, which cannot accessibly restrain the undesirable sawtooth effects. The GR method and ANR method exploit the pre-computed regressions to transform a LR patch into an HR patch. Although they can produce more details, there exist a great many unsatisfying jaggy artifacts around the edge and texture regions. The NE+LLE method assumes that the HR patch is restored, relying on the weighted linear combination of its K nearest neighbors estimated by corresponding LR neighbors, which also produces blurring edges and unpleasing details. The SRCNN method and RFL method are capable of generating clearer edges and relatively better visual effects in comparison with the previous methods. The reconstruction results of our algorithm with regard to edge, texture, and text regions are superior to the previous six algorithms, and the visual effects are further improved. For example, in the wing regions of the monarch image in Figure 1, the white spotted edges of the butterfly reconstructed by other algorithms are blurred. In comparison to other algorithms, the present algorithm removes the blurring phenomenon, and the reconstructed white spotted edges of butterfly are much clearer. For the texture part of the zebra image in Figure 2, there are different degrees of blurring in the white stripe edge regions of the reconstructed image by [PERSON]'s method, GR method, ANR method and NE+LLE method. Compared with other methods, the reconstructed zebra stripes using our algorithm are much brighter and clearer. For the text portions of the airplane image in Figure 3, compared with other methods, the reconstructed image obtained in this paper is much clearer, the blurring in text characters is eliminated, and the visual effect become better.