SATELLITE IMAGE RESOLUTION ENHANCEMENT USING DUAL TREE COMPLEX WAVELET TRANSFORM

    Resolution is an important feature in satellite imaging, which makes the resolution enhancement of such images to be of vital importance. As it was mentioned before, there are many applications of using satellite images, hence, resolution enhancement of such images will increase the quality of the other applications. The main loss of an image after being super-resolved by applying interpolation is on its high-frequency components (i.e., edges), which is due to the smoothing caused by interpolation. Hence, in order to increase the quality of the super-resolved image, preserving the edges is essential. In this design, DT-CWT has been employed in order to preserve the high-frequency components of the image. The DT-CWT has good directional selectivity and has the advantage over discrete wavelet transform (DWT). It also has limited redundancy. The DT-CWT is approximately shifting invariant, unlike the critically sampled DWT. The redundancy and shift-invariance of the DT-CWT mean that DT-CWT coefficients are inherently interpolable.
    In the proposed image resolution enhancement technique, DT-CWT is used to decompose an input image into different subband images. Six complex-valued high-frequency subband images contain the high-frequency components of the input image. In this design, the interpolation is applied to the high-frequency subband images. In the wavelet domain, the low-resolution image is obtained by the lowpass filtering of the high-resolution image. In other words, low-frequency subband images are the low resolution of the original image. Therefore, instead of using low-frequency subband images, which contain less information than the original input image, we are using the input image for the interpolation of two low-frequency subband images. Hence, using the input image instead of the low-frequency subband images increases the quality of the super-resolved image. Note that the input image is interpolated with half of the interpolation factor α(alpha) used to interpolate the high-frequency subbands, as illustrated in Fig. 1 on reference paper. The two upscaled images are generated by interpolating the low-resolution original input image and the shifted version of the input image in horizontal and vertical directions. These two real-valued images are used as the real and imaginary components of the interpolated complex LL image, respectively, for the IDT-CWT operation. By interpolating the input image by α/2 and the high-frequency subband images by α and then by applying IDT-CWT, the output image will contain sharper edges than the interpolated image obtained by interpolation of the input image directly. The design is performance is evaluated based on PSNR value.

Reference Paper: Satellite Image Resolution Enhancement Using Complex Wavelet Transform
Author’s Name:
Hasan Demirel and Gholamreza Anbarjafari
Source:
IEEE
Year:
2010
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