# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 5 Experimental Results
This group of experiments comprised real-shot experiments, so the subjective evaluation method was used to evaluate the restoration quality of the images. However, some existing image quality assessment (IQA) indicators were also used, such as the natural image quality evaluator (NIQE: the smaller the value, the smaller the gap with the natural image, and the better the image quality [25]), and the average gradient (the larger the value, the clearer the image texture). These two indicators were, thus, able to evaluate the image restoration quality from other perspectives. The comparison methods were [PERSON] and [PERSON]'s methods. The main reason for not choosing other methods was because other algorithms were more complicated to set up and had less reference value for the results run on the local machine. Moreover, the [PERSON] algorithm was more advanced, as seen in previous comparisons of works with space-variation images. Thus, the open-source program required only one major parameter, kernel size, so the results of [PERSON] method were more reliable if computed on this machine. The results are shown below in the order:  
1. The restoration of whole images in Figure 10;
2. The kernel result and the MFT in Figure 11;
3. The details of restoration in Figure 12;
4. The IQA of the results in Table 7;  
Discussion: The experimental results are shown in Figures 10 and 11. First, by looking at the morphology of the blur kernels, it can be seen that the blur kernels obtained by the split block method show different blurring states, depending on the region. Therefore, if the blur kernel of the whole image is estimated according to the premise of space-invariance, it can be seen that the morphology of the kernel from the comparison methods was similar, and the results also show the main blurred motion pattern of the whole image. However, since the blurring of the image is non-uniform, the comparison algorithms were able to obtain good results in relative regions, where the blurring patterns match well. However, a poor result was obtained in regions where the blurring patterns were widely disparate. Therefore, in Figure 12, this paper selected the parts where the blur kernel morphology differed relatively more from the comparison algorithm for comparison. In Figure 11, in the region of the green box on the top right corner of the figure, it can be seen that the blur state estimated by the proposed method is characterized by a predominantly horizontal motion with a vertical orientation, while the morphology estimated by the comparison method is an oblique blur. This difference performance of the detail restoration was caused by the different ability of the ringing effect control in the recovery results. Within the circular region in the lower part of the image, the corresponding blurred region is the intersection of the four blurred kernels in this method; therefore, the recoveredimage of this block reflects the common morphology of the four regional blurred kernels. It can be seen that an oblique motion is the main common feature of all four kernels. Thus, the results of this algorithm are only slightly better than [PERSON]'s in terms of ringing effects and better than those of [PERSON]'s algorithm in terms of detail. In the lower-left corner of the image, the blur shows a greater range of movement in the horizontal direction, according to the estimation results of this algorithm. However, the energy distribution of the blur kernels is mainly in the vertical direction. Therefore, [PERSON]'s method has a serious loss of detail in the image, to ensure control ability of the ringing. The results of [PERSON]. [PERSON]'s method are not sufficient in the lateral direction. In data \"6\" and \"8\", it shows bad ringing control in the oblique direction, and the same is shown in the location of the line cluster above.