When collecting images, owing to the influence of shooting equipment, shooting environment, and other factors, often low-illumination images with insufficient exposure are obtained. For low-illumination images, it is necessary to improve the contrast. In this paper, a digital color image contrast enhancement method based on luminance weight adjustment is proposed. This method improves the contrast of the image and maintains the detail and nature of the image. In the proposed method, the illumination of the histogram equalization image and the adaptive gamma correction with weighted distribution image are adjusted by the luminance weight of w1 to obtain a detailed image of the bright areas. Thereafter, the suppressed multi-scale retinex (MSR) is used to process the input image and obtain a detailed image of the dark areas. Finally, the luminance weight w2 is used to adjust the illumination component of the detailed images of the bright and dark areas, respectively, to obtain the output image. The experimental results show that the proposed method can enhance the details of the input image and avoid excessive enhancement of contrast, which maintains the naturalness of the input image well. Furthermore, we used the discrete entropy and lightness order error function to perform a numerical evaluation to verify the effectiveness of the proposed method.

As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.

A new projection-based color-to-gray conversion method is proposed in this letter. In the proposed method, an objective function which considers color contrasts in an input image is defined. Projection coefficients are determined by minimizing the objective function. Experimental results show the validity of the proposed method.

A new signed color distance for color-to-gray conversion is proposed. It is suited to reflect gradation and detailed color change in an input color image into an output monochrome image. Experiments show the effectiveness of the proposed distance.

In the impulse noise removal from a color image, vector filters are suitable for suppressing false color generation. However, the vector filters do not select optimal vectors to restore noise corrupted pixels. To cope with this problem, a cost function-based vector filter is proposed in this letter.

For the impulse noise removal from a digital image, most of existing methods cannot repair line structures in an input image. In this letter, a method which considers the local line structure is proposed. In order to judge the direction of the line structure, adjacent lines are considered. The effectiveness of the proposed filter is shown by experiments.

Protanopes and deuteranopes are difficult to distinguish some color pairs. In this letter, a new lightness modification method which considers the Craik-O'Brien effect is proposed. The lightness modification is performed at parts which are difficult to distinguish in the protanopia or deuteranopia. Experiments show the validity of the proposed method.