Image Denoising Based on Soft Computing Techniques

Image Denoising Based on Soft Computing Techniques

Abstract:
Image Denoising is one of the existing problems in research area. This paper presents an interactive algorithm for image Denoising and segmentation. This paper explains the task of segmenting any given color image using soft computing techniques. The segmentation techniques used are Fuzzy Clustering (FC), Fuzzy C Means (FCM) clustering and Convolutional Networks (CN). After the image is segmented, the noise can be removed by using bilateral filtering. The denoised images are compared using image quality metrics. The image quality metrics are Peak Signal to Noise Ratio (PSNR), and Mean Average Error (MAE). The time taken for Denoising is also used as a comparison parameter. The techniques have been tested with images of different size and resolution and the results are proven to be better than the existing state-of-art algorithms.

Problems In Remote Sensing – Graphic Arts Essay

Problems In Remote Sensing – Graphic Arts Essay
The predicaments in most remotely sensed data is affected by several common factors, such as error, uncertainty and scale. “The goal of remote sensing is to

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