This paper presents fast and effective filtering techniques for image enhancement from remote sensing Indian remote sensing satellite P6 Liss IV remotely sensed data like Near-Infrared band. There are four filtering techniques used for image enhancement based on spatial domain filters and frequency domain filters such as median filter, wiener filter, bilateral filter and Gaussian homomorphic filter and selected noises salt and pepper and Gaussian noise used with filter. Selected images tested with each filter and based on PSNR performance metric value and best filtering technique identified from these filters. Finally, Gaussian homomorphic filtering technique is suitable for image enhancement of the Liss IV remotely sensed Near-Infrared band. Image enhancement technique is preprocessing for future work such as edge detection and image segmentation.
In this paper the image processing method is used to enhance the pneumonia bacteria images. This paper recognized the bacteria images based on two domains. The enhancement techniques used for bacteria image enhancement were median filter, wiener filter, single scale retinex and multiscale retinex. Image enhancement has a very important role in digital image processing. The median and wiener filters were used for grayscale image enhancement. Then single scale retinex and multiscale retinex were used for color image enhancement. Based on performance metrics identified median filter is suitable for bacteria images in grayscale image enhancement and multiscale retinex is suitable for bacteria color image enhancement (Tab. 2, Fig. 8, Ref. 21).
With the accessibility of healthcare data for a significant proportion of patients in hospitals, using predictive analytics to detect diseases earlier has become more feasible. Identifying and recording key variables that contribute to a specific medical condition is one of the most difficult challenges for early detection and timely treatment of diseases. Conditions such as infertility that are difficult to detect or diagnose can now be diagnosed with greater accuracy with the help of predictive modeling. Infertility detection, particularly in females, has recently gained attention. In this work, the researchers proposed an intelligent prediction for female infertility (PreFI). The researchers use 26 variables for the early diagnosis and determine a subset of these 26 variables as biomarkers. These biomarkers contribute significantly to a better prediction of the problem. The researchers designed PreFI using ensemble methods with biomarkers and improved the performance of the predictive system.
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