Perceptually optimized subspace estimation for missing texture reconstruction

Takahiro Ogawa, M. Haseyama
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引用次数: 1

Abstract

This paper presents a perceptually optimized subspace estimation method for missing texture reconstruction. The proposed method calculates the optimal subspace of known patches within a target image based on structural similarity (SSIM) index instead of calculating mean square error (MSE)-based eigenspace. Furthermore, from the obtained subspace, missing texture reconstruction whose results maximize the SSIM index is performed. In this approach, the non-convex maximization problem is reformulated as a quasi convex problem, and the reconstruction of the missing textures becomes feasible. Experimental results show that our method overcomes previously reported MSE-based reconstruction methods.
感知优化缺失纹理重建子空间估计
提出了一种基于感知优化的缺失纹理重建子空间估计方法。该方法基于结构相似度(SSIM)指数计算目标图像中已知斑块的最优子空间,而不是基于均方误差(MSE)计算特征空间。然后,在得到的子空间中进行缺失纹理重建,其结果使SSIM索引最大化。该方法将非凸最大化问题重新表述为拟凸问题,使得缺失纹理的重建变得可行。实验结果表明,我们的方法克服了先前报道的基于mse的重建方法。
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