Intensity bound limit filter for high density impulse noise removal

Abstract

Digital images captured by electronic products are highly susceptible to salt & pepper noise during image acquisition, enrolment, preparation, and transmission phases. Therefore, it is essential to utilize superior image restoration methods to mitigate these effects. Additionally, in the restoration process, the preservation of edge data is essential as overall image quality can be severely degraded if the edge restoration processes underperform. In this paper, a novel two-stage intensity bound limit filter is proposed in which the denoised image is obtained via first stage generation of Intensity bound limit images and second stage recombination of the generated bound images. An interesting point to note is that the bound images preserve vital image edge data by extracting the infimum and supremum pixel values for any locality in the image. These separated bound images are subsequently utilized in a recombination stage to obtain the filtered image. Using this method, significant improvements in the boundary estimation are achieved especially in higher noise densities. Qualitative and quantitative analyses have been performed for standard, medical, and the Kodak image dataset which contains multiple colored images. Results show that the proposed algorithm outperforms state-of-the-art filters in terms of image detail restoration and overall noise removal. With respect to peak signal to noise ratio, an average improvement of 0.76 dB for standard images, 0.9 dB for medical images, and 1.03 db for Kodak dataset has been observed. A high-level hardware architecture has also been provided for the same.