بهبود رزولوشن تصاویر توسط درون یابی و اجزای دامنه موجک DWT-SWT

ABSTRACT Resolution Enhancement of images with Interpolation and DWT-SWT Wavelet Domain Components

Image resolution is always a key feature for all kinds of images. With ever increasing sizes of the displays need for super resolution images has also been increased. This is also impacted by the limited size of the digital image sensor. Though widespread commercial cameras provide very high resolution images, most of the scientific cameras still have the resolution of only 512 X 512. Resolution enhancement is always being associated with the interpolation techniques. Research suggests that interpolation methods increase the intensity of low frequency components. This means interpolated image will have less number of sharp intensity transactions per pixel. A new method for resolution enhancement which preserves high frequency contents of the image is suggested in the paper.

Spatial domain techniques lag in extraction and preservation of high frequency components of an image. This suggests that some other technique not involving spatial domain is to be used. So the image needs to be converted to some other domain, processed and then converted back to spatial domain. The domain can be Fourier domain, wavelet domain or any other. Fourier domain is more suitable for spectral filtering. The spectral filtering removes particular frequencies from the image. Wavelet domain separates components of an image in to individual matrices. These matrices then can be processed separately and combined together to get the desired result.

Fast algorithms for implementation of discrete wavelet transform have enhanced the use of wavelet domain for image resolution improvement. Various image processing algorithms can be implemented with discrete wavelet transform (DWT)[1]. DWT decomposes image into four sub bands. These sub bands are low-low (LL), low-high (LH), high-low (HL) and high-high (HH). These sub bands are of . half the dimensions of that of image under consideration. Stationary wavelet transform (SWT) is also being used for the image resolution enhancement [2]. SWT also has four sub bands similar to DWT but sub bands in SWT are of same size of that of the image. Here we have proposed a new method for image resolution enhancement which is based on combination of DWT and SWT components and interpolation. Also we have proved that our proposed technique is better compared to previously available techniques for resolution improvements.

In section II, a literature review for image resolution enhancement techniques has been given. In section III, the proposed method is described in detail. Results are demonstrated in section IV and concluding remarks are presented in section V.