Pages: 7 (2027 words) Published: July 11, 2013


IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition Hasan Demirel and Gholamreza Anbarjafari

Abstract—In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. Index Terms—Discrete wavelet transform, image super resolution, stationary wavelet transform.

tion enhancement techniques. The conventional techniques used are the following: — interpolation techniques: bilinear interpolation and bicubic interpolation; — wavelet zero padding (WZP). The state-of-art techniques used for comparison purposes are the following: — regularity-preserving image interpolation [7]; — new edge-directed interpolation (NEDI) [10]; — hidden Markov model (HMM) [11]; — HMM-based image super resolution (HMM SR) [12]; — WZP and cycle-spinning (WZP-CS) [13]; — WZP, CS, and edge rectification (WZP-CS-ER) [14]; — DWT based super resolution (DWT SR) [15]; — complex wavelet transform based super resolution (CWT SR) [5]. According to the quantitative and qualitative experimental results, the proposed technique over performs the aforementioned conventional and state-of-art techniques for image resolution enhancement. II. PROPOSED IMAGE RESOLUTION ENHANCEMENT In image resolution enhancement by using interpolation the main loss is on its high frequency components (i.e., edges), which is due to the smoothing caused by interpolation. In order to increase the quality of the super resolved image, preserving the edges is essential. In this work, DWT has been employed in order to preserve the high frequency components of the image. The redundancy and shift invariance of the DWT mean that DWT coefficients are inherently interpolable [9]. In this correspondence, one level DWT (with Daubechies 9/7 as wavelet function) is used to decompose an input image into different subband images. Three high frequency subbands (LH, HL, and HH) contain the high frequency components of the input image. In the proposed technique, bicubic interpolation with enlargement factor of 2 is applied to high frequency subband images. Downsampling in each of the DWT subbands causes information loss in the respective subbands. That is why SWT is employed to minimize this loss. The interpolated high frequency subbands and the SWT high frequency subbands have the same size which means they can be added with each other. The new corrected high frequency subbands can be interpolated further for higher enlargement. Also it is known that in the wavelet domain, the low resolution image is obtained by lowpass filtering of the high resolution image [16]. In other words, low frequency subband is the low resolution of the original image. Therefore, instead of using low frequency subband, which contains less information than the original high resolution image, we are using the input image for the interpolation of low frequency subband image. Using input image instead of low frequency subband increases the quality of the super resolved image. Fig. 1 illustrates the block diagram of the proposed image resolution enhancement technique. By interpolating input image by =2, and high frequency subbands by 2 and in the...

References: [1] L. Yi-bo, X. Hong, and Z. Sen-yue, “The wrinkle generation method for facial reconstruction based on extraction of partition wrinkle line features and fractal interpolation,” in Proc. 4th Int. Conf. Image Graph., Aug. 22–24, 2007, pp. 933–937. [2] Y. Rener, J. Wei, and C. Ken, “Downsample-based multiple description coding and post-processing of decoding,” in Proc. 27th Chinese Control Conf., Jul. 16–18, 2008, pp. 253–256. [3] H. Demirel, G. Anbarjafari, and S. Izadpanahi, “Improved motionbased localized super resolution technique using discrete wavelet transform for low resolution video enhancement,” in Proc. 17th Eur. Signal Process. Conf., Glasgow, Scotland, Aug. 2009, pp. 1097–1101.
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