Image Inpainting in 30 Years: A Survey

Engineering notes

Furthermore, we compile standard benchmarks, evaluation metrics and quantitative performance comparisons of representative algorithms.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

ABSTRACT As a fundamental task in restoring continuous visual signals, image inpainting plays a critical role in autonomous driving perception, medical imaging, video editing and digital heritage preservation. Driven by deep learning and large‐scale generative models, the field has transitioned from low‐level texture synthesis to high‐level semantic generation, yielding major breakthroughs in structural fidelity and visual realism. Centring on the generative paradigm as the architectural trajectory, this survey systematically categorizes the 30‐year evolution of image inpainting into three distinct technological generations: traditional prior‐driven synthesis, deep learning data‐driven reconstruction and modern foundation model‐driven generation. Despite this progress, highly competitive methods still struggle with large‐scale missing regions, global consistency in complex scenes, fine‐grained micro‐details and alignment with human visual perception. To address these gaps, we critically evaluate the technical paradigms and main bottlenecks within each of these evolutionary stages. We categorize and compare mainstream breakthroughs across high‐resolution restoration, text‐guided synthesis and complex scene generation. Furthermore, we compile standard benchmarks, evaluation metrics and quantitative performance comparisons of representative algorithms. Finally, we dissect open challenges—focusing on cross‐scene generalization and evaluation metric alignment—and outline future trajectories, particularly the integration of inpainting with text‐guided foundation models, providing a definitive reference for future theoretical and engineering advancements.

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