一种应用博弈和L0约束的盲图像修复方法

doi:10.19665/j.issn1001-2400.2021.04.014

Abstract

Abstract:

Image inpainting is the process of restoring the original image from the observed image with missing pixels using the prior information on the original image.Most image inpainting models assume that the missing areas of the image are known.However,inpractical applications,the information on these missing areas is difficult to obtain directly.In order to solve this problem,a new image inpainting model is established by using the sparse priori of L₀ norm and game theory.The new model is suitable for the two cases of known and unknown image missing areas.According to the structure of the objective function,an effective proximal alternating direction method of multipliers and a game-based alternating framework are proposed to solve the corresponding minimization problem,and the convergence of the model under certain conditions is analyzed.Compared with the existing inpainting models,numerical experiments show that the models and algorithms proposed can lead to better results and robustness insubjective and objective quality evaluation than the image inpainting methods available.

Key words: image inpainting, L₀ norm, alternating direction method of multipliers, peak signal to noise ratio, game

CLC Number:

TP391

FENG Xiangchu,WANG Ping,HE Ruiqiang. Method for inpainting blind images using the game and L0 constraint[J].Journal of Xidian University, 2021, 48(4): 103-112.

Figures/Tables 9

References 22

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缺失比例/%	观测图像的峰值信噪比	恢复图像的峰值信噪比
缺失比例/%	观测图像的峰值信噪比	O已知(算法2)	O未知(算法3)
10	17.261 0	44.434 7	44.423 5
20	14.278 0	40.868 6	40.859 9
30	12.512 6	38.538 6	38.529 6
40	11.263 2	36.488 3	36.483 6
50	10.289 6	34.618 1	34.616 5

缺失比例/%	观测图像的峰值信噪比	恢复图像的峰值信噪比
缺失比例/%	观测图像的峰值信噪比	O已知(算法2)	O未知(算法3)
10	17.248 8	45.832 2	45.773 7
20	14.272 6	42.371 3	42.347 3
30	12.509 4	40.062 7	40.050 4
40	11.261 1	38.093 5	38.087 4
50	10.288 3	36.262 1	36.249 1

缺失比例/%	观测图像的峰值信噪比	恢复图像的峰值信噪比
缺失比例/%	观测图像的峰值信噪比	L1TV_SBM	L0TV_PDA	文中模型(算法2)
10	15.586 0	33.585 0	42.439 3	46.495 8
20	12.582 6	31.428 9	39.355 2	42.386 5
30	10.812 3	28.748 6	36.873 6	39.508 8
40	9.570 6	24.711 2	34.415 1	36.899 2
50	8.600 9	19.162 4	32.165 9	34.573 2

缺失比例/%	观测图像的峰值信噪比	恢复图像的峰值信噪比
缺失比例/%	观测图像的峰值信噪比	L1TV_SBM	L0TV_PDA	文中模型(算法2)
10	15.581 2	33.627 6	47.955 5	49.735 6
20	12.580 4	31.895 0	43.551 7	45.306 3
30	10.811 0	28.766 9	40.339 4	42.338 0
40	9.569 7	24.907 7	37.252 7	39.652 6
50	8.600 4	19.128 0	34.385 9	37.271 6

缺失比例/%	观测图像的峰值信噪比	恢复图像的峰值信噪比
缺失比例/%	观测图像的峰值信噪比	L02TV_AOP	文中模型(算法3)
10	15.586 0	41.760 9	44.737 8
20	12.582 6	38.148 8	41.478 4
30	10.812 3	35.397 7	38.985 5
40	9.570 6	31.029 9	36.551 5
50	8.600 9	19.817 0	34.311 4

Method for inpainting blind images using the game and L0 constraint

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