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An Optimized Derivative Filter for Efficient Edge Detection of Gray Scale Image

Edge Detection is one of the most important tasks in the field of image processing. The edge of an image is a set of connected pixels that lie on the boundary between two regions. Noise is the major problem in the field of image processing. The images are affected by different types of noise. So it is most important task for edge mapping an image in the presence of noise. In image processing edge detection is a technique for mapping edge of an image without removing significant parts of the image contents, such as edges, lines or other details that are important to represent the quality of the image. To acquire a better performance we state an another derivative filter that works efficiently for edge mapping an image without removing significant parts of Image between. To evaluate the performance we calculate the Signal to Noise Ratio, the Peak Signal to Noise Ratio, the Root Mean Square Error, Image Fidelity and Measuring Similarity between two images. This edge detection operator gives better result with comparison to other existing edge detection operators.