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We propose an algorithm for image de-noising which uses a trimmed global mean filter with rank order absolute differences to remove random-valued impulse noise, stripes, scratches and blotches. It is a two stage algorithm, in the first stage the corrupted candidate is detected using rank ordered absolute differences (ROAD). In the second stage, the corrupted pixels are replaced by the median of the uncorrupted pixels in the selected window. Trimmed global mean filter is used if the selected window contains all the pixels as noisy candidate. We used a fixed window size in both detection and filtering stages. The visual and quantitative results show that proposed filter outperforms the existing filters in restoring image which is corrupted by random valued impulse noise. The proposed algorithm also provides good results over the impulse noise image corrupted by artificially introduced scratches, blotches, and stripes without causing blurring.

Keeping in view the variety of the applications, image denoising still remains the unexplored territory for the researchers. There are many pros and cons in existing denoising algorithms. The two prime cons of image denoising algorithms are (i) Over and under detection of noisy pixels (ii) Low performance at high noise levels. So, in order to overcome these existing issues, a spatially adaptive image denoising via enhanced noise detection method (SAID-END) is proposed for grayscale and color images. The denoising is achieved using a two-stage sequential algorithm, the first stage ensures accurate noise estimation by eliminating over and under detection of noisy pixels. The second stage performs image restoration by considering non-noisy pixels in estimation of the original pixel value. To enhance the accuracy while denoising high-density impulse noise and artifacts, both noise estimation and restoration stages are using a spatially adaptive window (window expands to spatially connected area), the size of the window depends upon the noise level in the vicinity of the reference noisy pixel. The two stages of the proposed method are referred to as (i) Enhanced adaptive noise detection (ii) Non-corrupted pixel sensitive adaptive image restoration. The proposed method is evaluated by two test steps to ensure its versatility and robustness. In the first step, the proposed method is tested on a wide standard data set of color and grayscale images affected by impulse noise and artifacts. The results of proposed method are compared with well-known methods compatible for denoising impulse noise and artifacts. In the second step, the results of proposed method are compared with the recent state of the art algorithms for traditional test images. The result shows that the proposed method outperforms the existing denoising methods when applied to grayscale and color images.

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