Low contrast detection factor based contrast enhancement and restoration for underwater images

7-13Marine ecosystem is the largest of earth’s aquatic ecosystems. It includes salt marshes, coral reefs, deep sea, sea floor, etc. To learn deep about the activities taking place inside, underwater imaging is a tool. But these images lack in contrast and brightness leading to the lack of information in the ocean activities. To enhance such low contrast underwater images, Low Contrast Detection Factor (LCDF) is proposed in this study. It uses the value, saturation and hue to enhance the low contrast regions and to restore the color. Quality assessment is done to substantiate the proposed algorithm. It is found that the entropy gives an average of 7.3. No-reference Quality Metrics such as Natural Image Quality Evaluator and Blind/ Referenceless Image Spatial Quality Evaluator shows an average value of 3.6 and 22.5, respectively. The blur metrics shows a value of 0.21. The quality metrics indicates that the naturalness of the underwater image is maintained while the contrast of the underwater image has increased

Low contrast detection factor based contrast enhancement and restoration for underwater images

Marine ecosystem is the largest of earth’s aquatic ecosystems. It includes salt marshes, coral reefs, deep sea, sea floor, etc. To learn deep about the activities taking place inside, underwater imaging is a tool. But these images lack in contrast and brightness leading to the lack of information in the ocean activities. To enhance such low contrast underwater images, Low Contrast Detection Factor (LCDF) is proposed in this study. It uses the value, saturation and hue to enhance the low contrast regions and to restore the color.  Quality assessment is done to substantiate the proposed algorithm. It is found that the entropy gives an average of 7.3. No-reference Quality Metrics such as Natural Image Quality Evaluator and Blind/ Referenceless Image Spatial Quality Evaluator shows an average value of 3.6 and 22.5, respectively. The blur metrics shows a value of 0.21. The quality metrics indicates that the naturalness of the underwater image is maintained while the contrast of the underwater image has increased

Image contrast enhancement based on intensity expansion-compression

In most image based applications, input images of high information content are required to ensure that satisfactory performances can be obtained from subsequent processes. Manipulating the intensity distribution is one of the popular methods that have been widely employed. However, this conventional procedure often generates undesirable artifacts and causes reductions in the information content. An approach based on expanding and compressing the intensity dynamic range is here proposed. By expanding the intensity according to the polarity of local edges, an intermediate image of continuous intensity spectrum is obtained. Then, by compressing this image to the allowed intensity dynamic range, an increase in information content is ensured. The combination of edge guided expansion with compression also enables the preservation of fine details contained in the input image. Experimental results show that the proposed method outperforms other approaches, which are based on histogram divisions and clippings, in terms of image contrast enhancement