International Journal of Computer and Applications

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

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Brief review of image denoising techniques

A review on CT image noise and its denoising

Image denoising review: From classical to state-of-the-art approaches

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics.

Latest trends on heart disease prediction using machine learning and image fusion

Smart agriculture sensors in IOT: A review

The impact of radiation dose is directly related to the quality of computed tomography (CT) images. Low-dose CT images are degraded with the noise and other factors. Noise reduction methods are very helpful to enhance the noisy CT images with a possibility to increase the signal-to-noise ratio (SNR) and have a scope for further reduction of radiation dose. In this study, a denoising scheme is proposed which is applicable only for two identical images with uncorrelated noise. In the proposed scheme, a non-local means (NLM) filter is used to denoise the first input image and a wavelet packet thresholding to denoise the second input image. Results of NLM filter are analysed and found excellent for noise suppression but missing the small structures of the input image. To recover that, the proposed scheme is using correlation-based wavelet packet thresholding. The final outcomes of the proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with existing methods and it is observed that performance of the proposed method is superior to existing methods in terms of visual quality, image quality index, peak SNR and entropy difference.

CT image denoising using NLM and correlation-based wavelet packet thresholding

Brain tumors are created by abnormal and uncontrolled cell division in brain itself. If the growth becomes more than 50%, then the patient is not able to recover. So the detection of brain tumor needs to be fast and accurate. The objective of this paper is to provide an efficient algorithm for detecting the edges of brain tumor. The first step starts with the acquisition of MRI scan of brain and then digital imaging techniques are applied for getting the exact location and size of tumor. MRI images consist of gray and white matter and the region containing tumor has more intensity. So, first noise filters are used for noise removal and then enhancement techniques are applied to the given MRI scan of brain. After that the basic morphological operations are applied for extracting the region suffering from tumor. And then verification of region detected is done by using watershed segmentation.

/pdf/application-of-edge-detection-for-brain-tumor-detection-umvosz3uqv.pdf

Manoj Diwakar

Papers

A review on CT image noise and its denoising

Latest trends on heart disease prediction using machine learning and image fusion

Smart agriculture sensors in IOT: A review

CT image denoising using NLM and correlation-based wavelet packet thresholding

Application of Edge Detection for Brain Tumor Detection