Region Based Laplacian Post-processing for Better 2-D Up-sampling

Abstract

Up-sampling plays an essential role while increasing the resolution of an image or a video intra frame through interpolation. Since the up-sampling process is analogous to a low pass filtering operation, it produces undesirable blurring artifacts that deteriorate the signal quality in terms of loss of fine details and critical edge information. To overcome this problem, a no reference, region adaptive, 2-D Laplacian based post processing technique is proposed here. The proposed method sharpens the Lanczos-3 based up-sampled video intra frame based on its region statistics so as to compensate the high frequency loss. Generally, in an image, the degree of blurring is more in the high variance regions as compared to the regions with low variance. Therefore, to restore the high frequency information, the sharpening should be more in the high variance regions than its low variance counterpart. Hence, 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.