Fractional Order Differentiator Based Filter For Edge Detection of Low Contrast Underwater Images
نویسندگان
چکیده
Underwater images usually suffer from degraded visibility. Light attenuates and scatters in water resulting in low contrast and haziness in the scenes. Therefore, the main problems to be dealt with in underwater environment are poor contrast, non-uniform lighting, haziness and blurring. Hence, in order to study underwater images, it becomes utmost important to extract the invisible or unclear edges. This paper presents an edge detection method by using fractional order differentiation (FOD) approach. As texture plays a major role in low-level image analysis, therefore texture based image enhancement is very important. In order to attain texture enhancement in images, an algorithm based on the Grünwald-Letnikov (G–L) fractional order derivative is proposed. Considering the G-L based fractional differential operator’s basic definition and implementation, a filter is devised and its applicability for texture enhancement is analyzed. Legendre polynomials based FOD has been used to design the filter. Further High Pass Filters (HPF) and Low Pass Filters (LPF) with the concept of intensity factor (γ) are designed. Next, a multiplication operation is performed in the pre processing stage. At last, Sobel’s method for edge detection is applied on the resultant pre processed image. The algorithm is experimented on various underwater images. The quality of the resultant images are evaluated by checking their respective Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) values. Further, the results are compared with another approach based on Riemann Liouville (R-L) fractional differential operator. The analysis of tests proves that the proposed method displays better results for detecting edges of low contrast underwater images and reveals more information than Histogram Equalization method and R-L method based on MSE and PSNR values. Keywords— Fractional Order Differentiator, Legendre polynomials, Grunwald Letnikov fractional derivative, MSE, PSNR, Sobel Edge Detector, Underwater Images.
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