Perceptually optimized subspace estimation for missing texture reconstruction

Takahiro Ogawa, Miki Haseyama

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.