This paper presents an image inpainting method based on sparse representations optimized with respect to a perceptual metric. In the proposed method, the structural similarity (SSIM) index is utilized as a criterion to optimize the representation performance of image data. Specifically, the proposed method enables the formulation of two important procedures in the sparse representation problem, 'estimation of sparse representation coefficients’ and 'update of the dictionary’, based on the SSIM index. Then, using the generated dictionary, approximation of target patches including missing areas via the SSIM-based sparse representation becomes feasible. Consequently, image inpainting for which procedures are totally derived from the SSIM index is realized. Experimental results show that the proposed method enables successful inpainting of missing areas.
CITATION STYLE
Ogawa, T., & Haseyama, M. (2013). Image inpainting based on sparse representations with a perceptual metric. EURASIP Journal on Advances in Signal Processing, 2013(1). https://doi.org/10.1186/1687-6180-2013-179
Mendeley helps you to discover research relevant for your work.