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The dark channel prior has been considered to be an efficient dehazing technique in recent years. However, its operation causes annoying halo effects. To solve this problem, many former imaging filters have been proposed and combined with the dark channel prior operation. However, these filters inevitably induce enormous computational burden while the dehazing effect of the dark channel prior still has room for improvement. To cope with this, a high-speed refinement method based on the gain intervention is proposed and combined with the dark channel prior to solve the aforementioned problems. As demonstrated in our experiments, the proposed filter integrated into the dark channel prior yields not only higher processing speeds but also superior recovery effects than can previous state-of-the-art imaging filters. More importantly, the dark channel prior combined with the proposed filter possesses the highest potential for practical application due to its superior dehazing effect and time complexity.

For the modern multimedia devices, display resolution and image quality are actively improved nowadays. Although such improvement can produce the high visual perception for the observer, the power consumption becomes an inevasible problem as it is rising progressively. In order to achieve a good balance between visual perception and power consumption, we propose a histogram-based power saving algorithm to improve the image contrast for OLED display panels. The proposed algorithm modifies the empty bins of the image histogram as a pre-process of power reduction. Furthermore, the visual effect is compensated using the power saving histogram equalization algorithm. Experimental results show that the proposed algorithm not only decreases the display power to be lower than that of compared algorithms, but also generates the highly perceptual contrast of the images.

Contrast enhancement involves transforming the intensity of pixels from the original state to feature significant impaction on many display devices, including laptops, PDAs, monitors, mobile camera phones, and so on. This paper proposes a new method to enhance the contrast of the input image and video based on Bezier curve. In order to enhance the quality and reduce the processing time, control points of the mapping curve are automatically calculated by Bezier curve which performs in dark and bright regions separately. Using the fast and accurate histogram modification allows the proposed method to transform the intensity well for both image and video. Experimental results demonstrate the effectiveness of the proposed method in providing a promising enhancement outcome with low computational cost.

Median filtering computation for noise removal is often used in impulse noise removal techniques, but the difficulties in removing high-density noise aspect restrict its development. In this paper, we propose a very efficient method to restore image corrupted by high-density impulse noise. First, the proposed method detects both the number and position of the noise-free pixels in the image. Next, the dilatation operation of the noise-free pixels based on morphological image processing is iteratively executed to replace the neighbor noise pixels until convergence. By doing so, the proposed method is capable to remove high-density noise and therefore reconstruct the noise-free image. Experimental results indicate that the proposed method more effectively removes high-density impulse noise in corrupted images in comparison with the other tested state-of-the-art methods. Additionally, the proposed method only requires moderate execution time to achieve optimal impulse noise removal.