# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results
### Experimental Results
#### 4.2.3 Domain Specific Images  
We further evaluate our method on two kinds of domain specific images, e.g., text and face images. Text images are rather challenging for most deblurring methods. Figure 17 displays the deblurring results on two text images. For fair comparison, the final deblurred results of both \(L^{1}/L^{2}\) kernel prior based methods and our method are recovered by the non-blind deblurring method [42]. Figure 17b shows the results by employing \(L^{1}\) kernel prior (\(\min_{k}\|\
abla u*k-\
abla f\|_{2}^{2}+\lambda\|k\|_{1}\)). Figure 17c shows the  
Figure 10: Quantitative evaluation on the dataset proposed by [PERSON] et al. [8]. Our method exceeds 70% success rate at the error ratio 4.6 and generates results having the highest average PSNR. results by using \(L^{2}\) kernel prior (\(\min_{k}\|\
abla u*k-\
abla f\|_{2}^{2}+\lambda\|k\|_{2}^{2}\)) and Figure 17d shows the results by the text deblurring method [15]. Comparing to the deblurred results using \(L^{1}/L^{2}\) kernel prior for the logo text image, our method estimates the kernel structure correctly while the other methods fail as shown in the top row of Figure 17. The bottom row of Figure 17 shows the deblurring results for a document text image. We note that our method generates better result than using \(L^{1}/L^{2}\) kernel prior and we obtain a comparable result with the specially designed text deblurring method [15]. Face images deblurring is challenging due to the blurry face images contain few edges or textures. Figure 18 shows some deblurring results on a face image. As can be seen, there are several artifacts in most of the results deblurred by other state-of-the-art methods, as shown in Figure 18b-k. Our method generates a clearer result with less ringing artifacts as shown in Figure 18l. Figure 11: Visual comparisons on two synthetic examples from the BSDS500 dataset [43]. Results are deblurred by [PERSON] and [PERSON] [1], [PERSON] et al. [11], [PERSON] et al. [10], [PERSON] et al. [29], [PERSON] and [PERSON] [13], [PERSON] and [PERSON] [14], [PERSON] et al. [3], [PERSON] et al. [23] and [PERSON] et al. [5], respectively. Figure 12: Visual comparisons on a real blurred image. (**a**) Blurred image. (**b**) [PERSON] et al. [10]. (**c**) [PERSON] and [PERSON] [14]. (**d**) Ours. The recovered image by our method is visually clearer than others. Figure 13: Comparisons on a real blurred image. (**a**) Blurred image. (**b**) [PERSON] and [PERSON] [1]. (**c**) [PERSON] et al. [10]. (**d**) [PERSON] et al. [29]. (**e**) [PERSON] and [PERSON] [13]. (**f**) [PERSON] and [PERSON] [14]. (**g**) [PERSON] et al. [3]. (**h**) [PERSON] et al. [15]. (**i**) [PERSON] et al. [23]. (**j**) [PERSON] et al. [5]. (**k**) [PERSON] et al. [17]. (**l**) Ours. Compared with other results, our deblurred result is more pleasing. Figure 14: Comparisons on a real blurred image. (**a**) Blurred image. (**b**) [PERSON] et al. [10]. (**c**) [PERSON] et al. [29]. (**d**) [PERSON] and [PERSON] [13]. (**e**) [PERSON] and [PERSON] [14]. (**f**) [PERSON] et al. [3]. (**g**) [PERSON] et al. [23]. (**h**) [PERSON] et al. [17]. (**i**) [PERSON] et al. [27]. (**j**) Ours. Compared with the state-of-the-art deblurring methods, our method generates a clear image with fine details. Figure 15: _Cont._  
Figure 16: Visual comparisons on a real wold blurred image with the state-of-the-art deep learning based methods. (**a**) Blurred image. (**b**) [PERSON] et al. [35]. (**c**) [PERSON] et al. [37]. (**d**) [PERSON] et al. [27]. (**e**) Ours. Our method performs favorably against the state-of-the-art deep learning methods. Figure 17: Comparisons on two text blurred images. (**a**) Blurred image. Results shown in (**b**–**e**) are deblurred by \(L^{1}\) kernel prior, \(L^{2}\) kernel prior, [PERSON] et al. [15] and ours, respectively. Our method generates better results than the previous two kernel prior methods and we obtain visually comparable or even better results than the specially designed for text deblurring method [15].