This paper proposes Optimized Dichromacy Projection (ODP) as a novel high-speed image recolouring technique for dichromacy compensation. Moreover, these methods cannot automatically resolve the trade-off between contrast enhancement and naturalness preservation. However, the high computational cost of these methods prevents their wide application in daily lives. Recently, researchers realized the importance of preserving naturalness in the processed images to avoid decreasing the quality of vision (QOV) and state-of-the-art image recolouring methods succeeded in realising both contrast enhancement and naturalness preservation by utilising optimisation model. To enhance their visual details, some methods have been developed to enhance colour contrast. People with colour vision deficiency (CVD) may have difficulty recognising or discriminating colours. The objective and subjective evaluation on three benchmark datasets demonstrates that our decolorization method is effective and competitive with some state-of-the-art decolorization methods. The proposed decolorization method is good at preserving low contrast as well as high contrast structures in the color image. Finally, we utilize a discrete searching solver to solve the optimization problem efficiently. Secondly, we propose to use the weighted normalized L1 norm to measure the distance between the grayscale image and the color image contrast features, and formulate an constrained optimization problem. Our main contribution is threefold: Firstly, we define a new contrast feature for a color image which combine the correlated information among R, G and B channels with the color contrast in each channel. In this paper, we propose a novel contrast preserving method for image decolorization. It is an important tool in image processing and realistic applications, such as monochrome printing and e-ink display. Image decolorization is to transform a color image into a grayscale image with the preserved contrast and consistent details. Third, we demonstrate that an effective luminance distribution can be achieved using our algorithm by using global and local tone mapping applications. Second, our optimal color conversion method produces luminance in images that are comparable to other state of the art methods which we quantified using the objective metrics (E-score and C2G-SSIM) and a subjective user study. Our main contribution is threefold: First, we implement a high-speed color processing method using exact pixel by pixel processing, and we report a $5.7\times$ speed up when compared to other new algorithms. In this proposed color to gray conversion model, we implement a weighted blending function to combine red (perceived warm) and blue (perceived cool) channel. This phenomena creates a perception of warm colors "advancing" toward the eye, while the cool colors to be "receding" away. It causes some rays corresponding to cooler colors (like blue, green) to converge before the warmer colors (like red, orange). Chromatic aberration results from differential refraction of light depending on its wavelength. In this paper, we propose a light-weight and high-speed image decolorization method based on human perception of color temperatures.
However, some image information is lost when converting from color to grayscale.
Grayscale images are fundamental to many image processing applications like data compression, feature extraction, printing and tone mapping.
0 kommentar(er)
