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Dark Channel and Laplace Based Under Water Image Restoration

Archana Singh




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As the digital world is increasing day by day so number of digital image processing issues are cover by different researchers. Out of those this work focus on under water noise removal which is also known as visibility restoration refers to different methods that aim to reduce or remove the degradation that have occurred while the digital image was being obtained. This paper has utilize the Laplace base distortion detection with dark channel technique for image restoration. Combination of both these techniques help in identifying the actual color values present in the original image scene. Experiment is done on many images of different environment or category. Results shows that LEDCR (Laplace Edge Based Dark channel Restoration) is better as compare to CBF.

This paper focus on the digital hazy image restoration. Here image store the edge region of the image then apply Laplace distribution for pixel value restoration. Here whole work is explained in fig.  Proposed work is trm as LEDCR (Laplace Edge Based Dark channel Restoration)

Here as the image is the collection of pixels where each pixel is representing a number that is reflecting a number over there now for each number depend on the format it has its range. So read a image means making a matrix of the same dimension of the image then fill the matrix correspond to the pixel value of the image at the cell in the matrix.

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Software Requirement :   MATLAB

Hardware Requirement :   • Intel Processor 2.0 GHz or above. • 2 GB RAM or more. • 160 GB or more Hard Disk Drive or above.

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