ON IMAGE-DRIVEN CHOICE OF WAVELET BASIS FOR

SUPER RESOLUTION Neeraj Kumar, Amit Sethi

Indian Institute of Technology Guwahati

METHODOLY • We started with the hypothesis that higher edge densities in an image are reconstructed by wavelets

with higher number of vanishing moments and smaller support size- contradictory requirements!

• We examined SR (using TCEM) performance of different wavelets on image categories with different

details (edges) using PSNR and SSIM as evaluation metrics.

• The slope of log-power spectral density was used as proxy for the edge density.

• Experimental results strengthened our hypothesis and we also justified the statement that SSIM is

better perceptual metric than PSNR.

INTRODUCTION • Super Resolution (SR) is a signal processing approach for estimating High

Resolution (HR) image from given single or multiple Low Resolution (LR)

images.

• SR from single image is highly ill-posed problem due to vast solution space.

• Recently wavelet domain SR techniques based on HMM, TCEM, etc. have

appeared in the literature and have given better SR performance in

comparison to state-of-the art.

• However, proper choice of wavelet basis for better SR reconstruction still

remains a challenge, which we address in this paper.

RESULTS • We took five categories of images (text, interior design,

facial, landscape and satellite images) with different levels

of edge information based on their perceptual properties

and computed the average edge density measure (slope

of log PSD) for each image.

• Then, we computed SR reconstruction (using TCEM with

different wavelets) of LR images generated by applying

Gaussian blur and downsampling to available HR images.

• We compared the reconstructed HR image with the

corresponding original image using SSIM and PSNR.

• The results shown in Fig. 1-5 strengthen our hypothesis

Results shown in Fig. 2 confirms that SSIM is better

perceptual metric than PSNR (Results of satellite image

are not shown due to space constraint).