CDDA: color-dominant deep autoencoder for faster and efficient bilateral image filtering

• Published in: Signal, image and video processing Vol. 15; no. 6; pp. 1189 - 1195
• Main Authors: Das, Apurba, Shylaja, S. S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Color imagery; Computer Imaging; Computer Science; Image filters; Image Processing and Computer Vision; Multimedia Information Systems; Original Article; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; color sparseness; Range-domain filtering; fast algorithm; weighted histogram; Deep autoencoder
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-020-01848-4

Nonlinear processing of high-dimensional data is quite common in image filtering algorithms. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge due to its computational complexity. In this paper, we have proposed a solution utilizing both color sparseness and color dominance in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency-based deep encoded dominant color gamut, learned for different subject classes of images. The proposed bilateral filter has been proved to be efficient both in terms of psycho-visual quality and performance for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with the existing fast bilateral algorithms in terms of the CTQ (critical to quality) parameters.