Image deblurring using tri-segment intensity prior

Object Motion Deblurring in Single Image Under Static Background

“…In order to prove the effectiveness of the algorithm, this section discusses the subjective and objective assessments to compare the proposed method with other methods. Other methods include Zuo et al [20], Bai et al [26], Zhang et al [27], Gong et al [31], Pan et al [32]. In all experiments, we referred to the above algorithm, through a large number of experiments to verify, the final parameters were set to α = 2, λ = 0.5, γ = 0.001.…”

“…Therefore, when the above algorithm processes the motion blurred image of the object, the deblurring effect was not obvious, and the background damage was more serious, and the noise was obvious. Compared with the algorithms in [20], [26] and [27], the method proposed in this paper could recover the information of the blurred area in the image very well, as shown in Figs.4 (f)-9(f), The texture of the processed image edges was relatively clear, the noise in the smooth area was well suppressed. In this study, 30 subjects were selected for the subjective evaluation of non-reference quality.…”

“…Zuo et al proposed an iterative norm regularization and data-driven strategy under MAP framework which aimed to facilitate salient edges and obtain accurate blur kernels [20]. In order to improve the performance of blur kernel estimation, many regularization and image prior deblurring methods have been investigated [21]- [27]. Liu et al [21] proposed a L0 norm of first-order and second-order image gradients in order to standardize the final estimation results.…”

“…As shown in Figs. 4-9, images restored via the methods of Zuo et al [20], Bai et al [26], Zhang et al [27], Gong et al [31], Pan et al [32] and the proposed method were measured through visual evaluation of six motion blur images titled "License Plate," "Car Logo," "Wagon Advertisement," "Buddha Statue," "Parking Lot," and "Two Cars". To verify the authenticity of the proposed algorithm, the experimental images are motion blur of objects with different degrees of blur, including text blur, single-target and multi-target motion blur.…”

“…As shown in Figs. 4(b)(c)-9(b)(c), the image processed by the algorithms in [20], [26] and [27] did not recover the information of the blurred area of the object, the information in the background of the clean region was degraded. This was because the above algorithms cannot effectively estimate the blur kernels of different regions in the image and deblur the different regions of the image during the restoration of the motion blurred image of the object.…”