# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 4 Experimental Results
### Qualitative Comparisons  
Considering that human subjects are the ultimate judges of image quality, the visual quality of the denoised images is critical when evaluating a denoising algorithm. Figure 6 shows the noise-corrupted images of Monarch, Airplane, Lena and Baboon, whose noise variances were 25, 25, 60 and 60, respectively. Figures 7-10 show the denoised images of Monarch, Airplane, Lena and Baboon disposed by competing algorithms. BM3D and NLM tended to over-smooth the image, while K-SVD BM3D and EPLL were likely to generate artifacts when noise was high. Due to the learned NSS prior by superpixel clustering, the proposed algorithm was more robust against artifacts, and preserved the edge and texture areas better than the other algorithms. For example, in the Monarch image, the SC-SR preserved the edges of the veins on the butterfly's wings much better than the other algorithms. In the Airplane image, the SC-SR reconstructed the English alphabet on the wing of the aircraft more clearly than the other algorithms. In the Lena image, the SC-SR recovered more textures and edges on the hat than the other algorithms. In the Baboon image, the SC-SR preserved more fine texture of the hair of the baboon than other competing algorithms.  
Figure 5: The impact of cluster number on average PSNR, average SSIM, and average FOM of SC-SR.  
Figure 6: The four noise-destroyed images.  
Figure 8: Comparison of denoising results of the Airplane noisy image corrupted by additive white Gaussian noise \(\delta=25\): (**a**) Original image; (**b**) NLM: \(\text{PSNR}=28.17\) dB, \(\text{SSIM}=0.8286,\text{FOM}=0.6036\); (**c**) K-SVD: \(\text{PSNR}=30.97\) dB, \(\text{SSIM}=0.8719\), \(\text{FOM}=0.7306\); (**d**) BM3D: \(\text{PSNR}=31.44\) dB, \(\text{SSIM}=0.8833\), \(\text{FOM}=0.7419\); (**e**) EPLL: \(\text{PSNR}=31.27\) dB, \(\text{SSIM}=0.8794\), \(\text{FOM}=0.7904\); and (**f**) SC-SR: \(\text{PSNR}=31.45\) dB, \(\text{SSIM}=0.8865\), \(\text{FOM}=0.7638\).  
Figure 7: Comparison of denoising results of the Monarch noisy image corrupted by additive white Gaussian noise \(\delta=25\): (**a**) Original image; (**b**) nonlocal means (NLM): \(\text{PSNR}=26.43\) dB, \(\text{SSIM}=0.8336\), \(\text{FOM}=0.8066\); (**c**) K-SVD: \(\text{PSNR}=28.72\) dB, \(\text{SSIM}=0.8880\), \(\text{FOM}=0.8532\); (**d**) block-matching and 3D filtering (BM3D): \(\text{PSNR}=29.38\) dB, \(\text{SSIM}=0.9001\), \(\text{FOM}=0.9024\); (**e**) expected patch log likelihood (EPLL): \(\text{PSNR}=29.38\) dB, \(\text{SSIM}=0.9001\), \(\text{FOM}=0.9024\); and (**f**) SC-SR: \(\text{PSNR}=29.44\) dB, \(\text{SSIM}=0.9045\), \(\text{FOM}=0.9076\).  
Figure 10: Comparison of denoising results of the Baboon noisy image corrupted by additive white Gaussian noise \(\delta=60\): **(a)** Original image; **(b)** NLM: PSNR \(=20.95\) dB, \(\text{SSIM}=0.3568\), \(\text{FOM}=0.2680\); **(c)** K-SVD: PSNR \(=22.08\) dB, \(\text{SSIM}=0.4356\), \(\text{FOM}=0.3077\); **(d)** BM3D: PSNR \(=22.38\) dB, \(\text{SSIM}=0.4685\), \(\text{FOM}=0.3173\); **(e)** EPLL: \(\text{PSNR}=22.48\) dB, \(\text{SSIM}=0.4890\), \(\text{FOM}=0.4114\), and **(f)** SC-SR: \(\text{PSNR}=22.56\) dB, \(\text{SSIM}=0.4901\), \(\text{FOM}=0.4358\).  
Figure 9: Comparison of denoising results of the Lena noisy image corrupted by additive white Gaussian noise \(\delta=60\): **(a)** Original image; **(b)** NLM: \(\text{PSNR}=24.59\) dB, \(\text{SSIM}=0.6999\), \(\text{FOM}=0.2777\); **(c)** K-SVD: \(\text{PSNR}=26.90\) dB, \(\text{SSIM}=0.7328\), \(\text{FOM}=0.3298\); **(d)** BM3D: \(\text{PSNR}=27.98\) dB, \(\text{SSIM}=0.7636\), \(\text{FOM}=0.4394\); **(e)** EPLL: \(\text{PSNR}=27.60\) dB, \(\text{SSIM}=0.7465\), \(\text{FOM}=0.5013\); and **(f)** SC-SR: \(\text{PSNR}=28.05\) dB, \(\text{SSIM}=0.7871\), \(\text{FOM}=0.4753\).