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Document details - Wavelet Coefficients Augmentation and Average Segmentation based Image Super Resolution

Journal Volume 10, Issue 4, April 2021, Article 16413224 Prathibha Kiran, Fathima Jabeen , " Wavelet Coefficients Augmentation and Average Segmentation based Image Super Resolution" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 10, Issue 4, April 2021 , pp. 026-039 , ISSN 2319 - 4847.

Wavelet Coefficients Augmentation and Average Segmentation based Image Super Resolution

    Prathibha Kiran, Fathima Jabeen

Abstract

Abstract The recent advancement in digital image processing software has seen a surge in image super-resolution algorithms aimed at several applications. The research paper, propose wavelet coefficients augmentation and average segmentation based image super-resolution. The different kinds of images are considered and resized to uniform size. The Discrete Wavelet Transform (DWT) is applied to obtain four bands with each band size of 83x152. The Low Resolution (LR) images are obtained by combining four bands and divide each coefficient by two to test the method. The High Resolution (HR) images are obtained by inflating one coefficient into 2x2 coefficients from LR images. The 3x3 overlapped segmentation is done on HR matrix and average value of each 3x3 matrix is computed and replace 3x3 matrix by average value. The guided filter to enhance the superiority of image is used by sharpening image. The LL band coefficients of original image is padded with zeros to obtain matrix size of 166x304 and use IDWT to get back image. The guided filter image and IDWT images are combined in fusion 2 by taking average value to obtain Super Resolution (SR) image. It is noticed that the performance of the projected technique is improved compared to current approaches. Keywords: DWT, Super Resolution, Segmentation, Fusion, Image Processing

  • ISSN: 23194847
  • Source Type: Journal
  • Original language: English

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