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This paper presents a missing texture reconstruction method based on projection onto convex sets (POCS). The proposed method classifies textures within the target image into some clusters in a high-dimensional texture feature space. Further, for the target missing texture, our method performs a novel approach, that monitors the errors caused by the POCS algorithm in the feature space, and adaptively selects the optimal cluster including similar textures. Then, the missing texture is restored from these similar textures by a new POCS-based nonlinear subspace projection scheme. Consequently, since the proposed method realizes the nonconventional adaptive technique using the optimal nonlinear subspace, the accurate restoration result can be obtained. Experimental results show that our method achieves higher performance than the traditional method.