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IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 5, MAY 2011
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
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 5, MAY 2011
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