Open AccessRole of recursive cubic spline interpolation method and convolution filter in image processing
Author(s) -
N Gajalakshmi,
S. Karunanithi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/2/022027
Subject(s) - filter (signal processing) , convolution (computer science) , artificial intelligence , computer vision , feature (linguistics) , mathematics , computer science , algorithm , pixel , composite image filter , image (mathematics) , image scaling , image processing , linguistics , philosophy , artificial neural network
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.