Interpolation of images using discrete wavelet transform to simulate image resizing as in human vision

Abstract

This paper presents discrete wavelet transform (DWT) and its inverse (IDWT) with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human observer moves closer to or farther from a scene, the retinal image of the scene zooms in or out, respectively. This zooming in or out can be modeled using variable scale interpolation. The paper proposes a novel way of applying DWT and IDWT in a piecewise manner by non-uniform down-or up-sampling of the images to achieve partially sampled versions of the images. The partially sampled versions are then aggregated to achieve the final variable scale interpolated images. The non-uniform down-or up-sampling here is a function of the required scale of interpolation. Appropriate zero padding is used to make the images suitable for the required non-uniform sampling and the subsequent interpolation to the required scale. The concept of zeroeth level DWT is introduced here, which works as the basis for interpolating the images to achieve bigger size than the original one. The main emphasis here is on the computation of variable size images at less computational load, without compromise of quality of images. The interpolated images to different sizes and the reconstructed images are benchmarked using the statistical parameters and visual comparison. It has been found that the proposed approach performs better as compared to bilinear and bicubic interpolation techniques.

5/3 Lifting Scheme Approach for Image Interpolation

“…The forward lifting scheme wavelet transform divides the data set being processed into an even half and an odd half [3].…”

Lung Cancer Recognition using Radon Transform and Adaptive Neuro Fuzzy Inference System

“…There are several interpolation techniques that have been documented in the past. The widely used techniques are nearest neighbor, bilinear, bicubic, B-splines, lanczos2, discrete wavelet transform, Kriging [19][20][21]; Bicubic interpolation technique is used in this study to obtain a scaled version of the Radon transformed image of size 8×8. The next step of vector features preparing is concatenate operation in which the rows of pixels in the resized image are concatenated resulting in a single row with 64 elements.…”

Successive Image Interpolation Using Lifting Scheme Approach

“…Here, a novel concept of image magnification and subsequent reconstruction is proposed. In this scheme, zeroeth level DWT (Asamwar et al, 2010) is used followed by IDWT for magnification of the images. Input Image is allowed to undergo zeroeth level discrete wavelet transformation which results in the four components (LPLP, LPHP, HPLP and HPHP).…”

“…Edge detection based on lifting scheme is discussed in (Ge et al, 2007). Image resizing algorithms using DWT are used to resize the image to any desired scale (Asamwar et al, 2010;2009). In the proposed work the Lifting scheme divides the complete data set into two equal parts that is even and odd based on the spatial sequence.…”