Role of recursive cubic spline interpolation method and convolution filter in image processing

Abstract

During this article, we suggested a RCSIM through Convolution filter. This filter explores the images to enhance and improve the efficiency of both gray scale and color images. Initially we identified the noises in the images and simultaneously, the corresponding pixels are noticed to improve that particular part of that image using edge detection algorithm. The pixel values of the image is converted as the window size of the matrix. This matrix should be a (n X n) square matrix and then we applied the convolution filtering techniques to the (« X n) matrix. The development of the (input) image through the convolution filter yields the feature map. The feature map accentuates the uniqueness of the original image. At the same time, the RCSIMis used to interrupt the mathematical data to the feature map. The proposed approach reduces the sound and protects the efficiency of the unique image.

Image Interolation Using of the Fifth and Seventh Order Polynomial Parameter Kernels

“…Kod procesiranja signala često je neophodno primeniti interpolaciju. Za rad u realnom vremenu najčešće se primenjuje konvoluciona interpolacija (Meijering et al, 2003), (Gajalakshmi et al, 2021) Konvoluciona interpolacija uniformno semplovanih podataka realizuje se primenom interpolacionog konvolucionog jezgra (Meijering et al, 2001). Po teoriji uzorkovanja sinc funkcija predstavlja idealno interpolaciono jezgro (Pratt, 1978).…”