A fast method for impulse noise reduction in digital color images using anomaly median filtering
Srinivasa Rao Gantenapalli, Praveen B. Choppala, Vandana Gullipalli, James Stephen Meka, Paul D. Teal · 2022
The traditional vector median filtering and its variants used to reduce impulse noise in digital color images operate by processing over all the pixels in the image sequentially. This renders these filtering methods computationally expensive. This paper presents a fast method for reducing impulse noise in digital color images. The key idea here is to slice each row of the image as a univariate data vector, identify impulse noise using anomaly detection schemes and then apply median filtering over these to restore the original image. This idea ensures fast filtering as only the noisy pixels are processed. Using simulations, we show that the proposed method scales efficiently with respect to accuracy and time. Through a combined measure of time and accuracy, we show that the proposed method exhibits nearly 42% improvement over the conventional ones.