Multispectral image denoising methods: A literature review

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The Multispectral images appear to give a more true representation of real-world scenes to improve the efficiency of many tasks involving computer vision and object extraction, identification and quantification, tagging operations and imagery segmentation, compared to standard RGB or grayscale images. The use of visible multi-spectral spectrum images as opposed to standard RGB systems enables high colour reproduction characteristics due to the achievement of minimal data from RGB images. Due to the restriction of facilities, restricted bandwidth and radiant energy losses, MSIs are impaired during capture by many noises. The formulation of a new mathematical definition of the deep study model for denoising is a complex investigation problem and several researchers have defined various MSI denoising algorithms or methods. Many researchers suggested its use as a sparse coding of noisy patches in the application of the neural network. These additionally allow different methods to change themselves with an algorithm for a mission. In reality, however, multiple noises often come across a multi-spectral image. We presented in this study the previous techniques for the MSI influenced by noise. The literature survey explains the description and advantages of previous approaches relative to each other.