Image Denoising Using Interquartile Range Filter with Local Averaging
This addresses noise reduction in images for applications like photography or medical imaging, but it is incremental as it builds on existing statistical filtering methods.
The paper tackled image denoising by using an interquartile range filter with local averaging to detect and replace noisy pixels, resulting in better edge preservation and higher PSNR compared to a median filter.
Image denoising is one of the fundamental problems in image processing. In this paper, a novel approach to suppress noise from the image is conducted by applying the interquartile range (IQR) which is one of the statistical methods used to detect outlier effect from a dataset. A window of size kXk was implemented to support IQR filter. Each pixel outside the IQR range of the kXk window is treated as noisy pixel. The estimation of the noisy pixels was obtained by local averaging. The essential advantage of applying IQR filter is to preserve edge sharpness better of the original image. A variety of test images have been used to support the proposed filter and PSNR was calculated and compared with median filter. The experimental results on standard test images demonstrate this filter is simpler and better performing than median filter.