DWT based Image Super Resolution using Segmentation and Edge Detection

An image super-resolution is the development of creating a high-resolution image from low-resolution images for better image visualization for several recent applications. In this research paper, we propose DWT-based Image Super Resolution using Segmentation and Edge Detection for better picturing of images. The different kinds of colore images are considered and converted into greyscale images and resized to a uniform size of 166x304. The Discrete Wavelet Transform (DWT) is used to get one low-frequency and three high-frequency bands with a size of 83x152. The four bands are fused and dividing each coefficient by two to get Low Resolution (LR) images. Each coefficient of the LR image is inflated into 2x2 coefficients to get High Resolution (HR) image. The HR matrix is segmented into a 3x3 overlapped matrix and an average value is calculated and assigned to the whole HR matrix. The Canny edge detection is used on the original image to get the edge detected image. The pixel values of HR and canny edge detected images are compared and high-value coefficients are considered. The guided filter is applied to the highvalue coefficients matrix to improve the quality of the image by refining the image. The LL band of the DWT matrix is padded with zeros to convert matrix size to 166x304 and Inverse Discrete Wavelet Transform (IDWT) is used on it. The refined and IDWT images are merged by computing the average values to acquire Super Resolution (SR) image. It is noted that the anticipated system results are enhanced compared to current methods. Keywords: DWT, Fusion, Image Processing, Segmentation, Super Resolution