An effective median filter for salt & pepper impulse range signals. An additive noise process may corrupt these noise removal is presented. This computationally efficient filtering digital images in both the acquisition and transmission stages. technique is implemented by a two pass algorithm: In the first Application-specific image filtering algorithms are needed to pass, identification of corrupted pixels that are to be filtered are simultaneously remove the effects of the corruptive process perfectly detected into a flag image using a variable sized detection window approach; In the second pass, using the and preserve important features of the images. Impulse noise detected flag image, the pixels to be modified are identified and removal in image processing often involves the removal of corrected by a more suitable median. Experimental results show these salt and pepper noises from images which is a very that the proposed algorithm performs far more superior than important pre-processing step for most other subsequent many of the median filtering techniques reported in terms of processing tasks such as edge detection, segmentation and retaining the fidelity of the image highly corrupted by impulse classification. In this area, early advances were dominated by noises even to the tune of ninety percent impulse noise. The linear filtering. They have had enormous impact on the proposed algorithm is free from patchy effects, does not extend lmelterf They techad formous stationary black or white blocks in the image as has been found in many development of various techniques for processing stationary other adaptive median based techniques and is very effective in and non-stationary signals. However, there are a large number cases when images are corrupted with large percentage of impulse of situations where linear filtering approach performs poorly. noises. This algorithm works very well for images with lower The limitation is the inability to simultaneously eradicate noise percentage of impulse noises also.
The paper presents an improved median filter for salt & pepper impulse noise removal. This computationally efficient filtering technique is implemented as a two pass algorithm: In the first pass, identification of corrupted pixels that are to be filtered are perfectly detected into a flag image using an iterative fixed sized smaller window approach; In the second pass, using the detected flag image, the pixels to be modified are identified and corrected by a valid median. Experimental results have shown that the proposed algorithm performs far more superior than many of the median filtering techniques reported in terms of retaining the fidelity of the image highly corrupted by impulse noises even to the tune of ninety percent impulse noise. The proposed algorithm is free from patchy effects, does not extend black or white blocks in the image as has been found in many other adaptive median based techniques and is very effective in cases when images are corrupted with large percentage of impulse noises. This algorithm works very well for images with lower percentage of impulse noises.
Soil is a major and important natural resource, which not only supports human life but also furnish commodities for ecological and economic growth. Ecological risk has posed a serious threat to the ecosystem by the degradation of soil. The high-stress level of heavy metals like chromium, copper, cadmium, etc. produce ecological risks which include: decrease in the fertility of the soil; reduction in crop yield & degradation of metabolism of living beings, and hence ecological health. The ecological risk associated, demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological and hence economic growth. This study reviews the efficient assessment models to predict soil heavy metal contamination.
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