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It was evaluated using eight different measures, some of which are reference-based, and the rest are blind-based. The proposed model was tested on 5141 images collected from various, well-known datasets. It operates in two modes (mode-1 and mode-2), both of which investigate a combination of contrast and chromaticity enhancement techniques.
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Second, decides about the suitable enhancement steps (i.e., mode of operation) to be performed. This paper proposes an image processing model which first, analyses the color characteristics of the degraded image. Using image processing techniques to enhance this kind of image remains very attractive because of its low-cost of implementation and typically its small number of parameters when compared to more complex learning techniques. Such environments provoke several combined degradations in images including color attenuation, blurring and low contrast. Įnhancing underwater images is a challenging problem owing to light scattering and absorption in underwater environments. The difference between L f and L b is also the luminance of the artificial light source that is to say, the luminance of the artificial light source. Higher mean luminance in the foreground of an image than in the background indicates the existence of an artificial light source. In an underwater image without artificial lighting, the background directly transmits natural light without reflecting off of objects and is therefore the brighter part of the image. The existence of artificial lighting can be determined by the difference between the mean luminance of the foreground and the background, L f and L b. If an artificial light source L is employed during image capturing process, the luminance contributed by L must be deducted first to avoid over-compensation for the stages followed, as illustrated in Fig. Artificial light sources are often supplemented to avoid insufficient lighting commonly encountered in underwater photographic environment, as shown in Fig. Color balance is restored for the whole image, rather than just the top portion of the frame. 7, the color shift suffered at the lower part of the image is greatly reduced. Figure 8 demonstrates the result of fine-tuning the amount of wavelength compensation by deriving the water depth of every image pixel.