Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

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Gutta Srinivas Rao, et. al.


The paper presents a novel algorithm for the computation of the image decomposition using a morphological filter with reconstruction. The target applications are image contrast enhancement especially those with high dynamic content. Both bright and dark regions contrast enhancement were considered. A new hardware efficient implementation of decomposition is presented. Following decomposition in 5 levels of detail a local contrast enhancement is performed. The new reconstruction algorithm and its hardware implementation as proposed is shown to be independent on structural element size and that it results in a predictable time frame operation. A mixed schematic and VHDL/Verilog description of the decomposition filters was synthesized and results show far higher speed performance compared with solutions found in recent literature.

Image organizing strategies are essentially required to the pictures captured by satellites. The grey levels in the image captured by satellites need to be normalized for improving picture contrast. The pictures captured by satellites are generally poor in quality and the contrast levels are also very less. In this manuscript a differentiation approach dependent on scientific morphology called rotational morphological transformations are proposed using Modified Opening by Reconstruction Method (MORM). This methodology upgrade pictures with poor contrast and unable to recognize the objects in the image. In the images with poor lightening, morphological operations are performed to improve its clarity and contrast. Picture enhancement has been done by applying morphological operations on the satellite images considered. The technique utilizes data from picture by squares, while the morphological methods change the strategy using the contrast enhancement activity, which is utilized to characterize the multi-foundation poor lightening pictures. The total picture handling procedure is implemented utilizing MATLAB reproduction model. The MORM method is compared with the traditional methods and the results show that the proposed method is better in improving the accuracy rate.

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