An image inpainting method based on multiple receptive fields and dynamic matching of damaged patterns

Jiahao MENG; Weirong LIU; Changhong SHI; Zhijun LI; Jie LIU

doi:10.3969/j.issn.1003-7985.2026.01.012

Journal of Southeast University (English Edition) ›› 2026, Vol. 42 ›› Issue (1) :121 -130. DOI: 10.3969/j.issn.1003-7985.2026.01.012

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An image inpainting method based on multiple receptive fields and dynamic matching of damaged patterns

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Abstract

Current image inpainting models are primarily designed to achieve a large receptive field （RF） using refinement networks to incorporate different scales. However, these models fail to adapt the use of different RFs to the specific patterns of image damage, resulting in artifacts and semantic information confusion in repaired images. To address the problems of artifacts and semantic information confusion, inspired by different sensitivities of different RFs to inpainting the same image damaged patterns, this study proposes an image inpainting method based on multiple receptive fields （MRFs） and dynamic matching of damaged patterns. First, the parallel filter banks are used to extract the MRF feature groups. Second, the features are dynamically weighted and screened, guided by the mask image, to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern. A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images. Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets: Paris StreetView, CelebA‐HQ, and Places2.

Keywords

image inpainting / generative adversarial networks / multiple receptive fields （MRFs） / dynamic matching of damaged patterns / decoder with fast Fourier convolutional

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Jiahao MENG, Weirong LIU, Changhong SHI, Zhijun LI, Jie LIU. An image inpainting method based on multiple receptive fields and dynamic matching of damaged patterns. Journal of Southeast University (English Edition), 2026, 42 (1) : 121-130 DOI:10.3969/j.issn.1003-7985.2026.01.012

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References

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[1]	WANG H, WANG X, LIAO R X, et al. Ship identification and tracking for navigable bridge based on improved YOLOv8 and ByteTrack[J]. Journal of Southeast University (Natural Science Edition), 2025, 55(5): 1380-1387. (in Chinese)

[2]	JIA J W, NI Y H, MAO J X, et al. Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision[J]. Journal of Southeast University (English Edition), 2025, 41(3): 278-285.

[3]	XIANG H Y, ZOU Q, ALI NAWAZ M, et al. Deep learning for image inpainting: A survey[J]. Pattern Recognition, 2023, 134: 109046.

[4]	HUANG J B, KANG S B, AHUJA N, et al. Image completion using planar structure guidance[J]. ACM Transactions on Graphics, 2014, 33(4): 1-10.

[5]	ZHAO X, WANG L M, ZHANG Y F, et al. A review of convolutional neural networks in computer vision[J]. Artificial Intelligence Review, 2024, 57(4): 99.

[6]	HUO G Y, SHENG X L, SHI S X. Unsupervised underwater image enhancement with global background light intensity estimation and contrast adjustment[J]. Journal of Southeast University (Natural Science Edition), 2025, 55(1): 297-305. (in Chinese)

[7]	LIU J L, GONG M G, TANG Z D, et al. Deep image inpainting with enhanced normalization and contextual attention[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(10): 6599-6614.

[8]	NAZERI K, NG E, JOSEPH T, et al. EdgeConnect: Structure guided image inpainting using edge prediction[C]// 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul, Korea, 2019: 3265-3274.

[9]	ZHANG R S, QUAN W Z, ZHANG Y, et al. W-net: Structure and texture interaction for image inpainting[J]. IEEE Transactions on Multimedia, 2022, 25: 7299-7310.

[10]	LI Y, ZHAI J, LU W, et al. Image inpainting with aggregated convolution progressive network[J]. IET Image Processing, 2025, 19(1): e13318.

[11]	LIU S, CHEN J Y, DING X L, et al. Progressive Reverse Attention Network for image inpainting detection and localization[J]. Computer Vision and Image Understanding, 2025, 259: 104407.

[12]	ZHENG C X, CHAM T J, CAI J F, et al. Bridging global context interactions for high-fidelity image completion[C]// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA, 2022: 11502-11512.

[13]	YU J H, LIN Z, YANG J M, et al. Generative image inpainting with contextual attention[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 5505-5514.

[14]	LIU H Y, JIANG B, SONG Y B, et al. Rethinking image inpainting via a mutual encoder-decoder with feature equalizations[M]//Computer Vision-ECCV 2020. Cham, Switzerland: Springer International Publishing, 2020: 725-741.

[15]	LIU G L, REDA F A, SHIH K J, et al. Image inpainting for irregular holes using partial convolutions[C]// Computer Vision-ECCV 2018. Cham, Switzerland: Springer, 2018: 89-105.

[16]	JOHNSON J, ALAHI A, LI F F. Perceptual losses for real-time style transfer and super-resolution[C]// Computer Vision-ECCV 2016. Cham, Switzerland: Springer, 2016: 694-711.

[17]	GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 2414-2423.

[18]	DOERSCH C, SINGH S, GUPTA A, et al. What makes paris look like paris?[J]. Communications of the ACM, 2015, 58(12): 103-110.

[19]	LIU Z W, LUO P, WANG X G, et al. Deep learning face attributes in the wild[C]// 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile, 2015: 3730-3738.

[20]	ZHOU B L, LAPEDRIZA A, KHOSLA A, et al. Places: A 10 million image database for scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(6): 1452-1464.

[21]	QUAN W Z, ZHANG R S, ZHANG Y, et al. Image inpainting with local and global refinement[J]. IEEE Transactions on Image Processing, 2022, 31: 2405-2420.

[22]	WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

[23]	HEUSEL M, RAMSAUER H, UNTERTHINER T, et al. GANs trained by a two time-scale update rule converge to a local nash equilibrium[C]// Proceedings of the International Conference on Neural Information Processing Systems. Long Beach, CA, USA, 2017: 6629-6640.

[24]	ZHANG R, ISOLA P, EFROS A A, et al. The unreasonable effectiveness of deep features as a perceptual metric[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 586-595.

[25]	ZHU M Y, HE D L, LI X, et al. Image inpainting by end-to-end cascaded refinement with mask awareness[J]. IEEE Transactions on Image Processing, 2021, 30: 4855-4866.

[26]	ZENG Y H, FU J L, CHAO H Y, et al. Aggregated contextual transformations for high-resolution image inpainting[J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(7): 3266-3280.

[27]	JAIN J, ZHOU Y Q, YU N, et al. Keys to better image inpainting: Structure and texture go hand in hand[C]//2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI, USA, 2023: 208-217.

[28]	ZUO Z W, ZHAO L, LI A L, et al. Generative image inpainting with segmentation confusion adversarial training and contrastive learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(3): 3888-3896.