Local Adaptive Laplacian for Better 2-D Up-sampling

Abstract

Most of the existing up-sampling techniques through interpolation produce undesirable blurring artefacts while converting a low resolution image to its high resolution counterpart. The blurring is caused by the loss of high frequency information during the down sampling and upsampling process. To overcome this problem, a no reference,local adaptive, 2-D Laplacian based post processing technique is proposed here. The proposed method sharpens the DCT (Discrete cosine transform) based up-sampled video intraframe based on its local statistics so as to compensate the high frequency loss. Generally, the degree of blurring is more in the high variance regions in an image. Therefore, to restore the high frequency information, the sharpening should be more in the high variance regions than the low variance regions. Therefore, in this proposed method, a 2-D Laplacian kernel is made adaptive as per the statistical local variance of a 3×3 neighbourhood. If the local variance is more, the central kernel weight becomes proportionately high and vice versa based on the direct mapping basis. The remaining pixel weights of the Laplacian kernel are adjusted as per the central pixel weight such that the sum of all weights in the adaptive Laplacian kernel is zero. Experimental results reveal that the proposed method outperforms most of the widely used existing interpolation techniques in terms of objective and subjective measures.