The Use of Digital Image Art under Visual Sensing Technology for Art Education

The traditional art education model often cannot achieve good teaching results due to the development of digital technology. Art is integrated with technology based on three-dimensional (3D) panoramic vision sensing technology, so as to study the application of the combination of visual sensing technology and digital image art in the field of art education. With the application of projection virtual game “Whac-A-Mole” as an example, the integration of panoramic color volume structured light generation technology with single emission point and omnidirectional visual imaging technology with single view is proposed; the mathematical model of the active vision system is established, and the camera model, projector model, object surface illumination model, and their relationship are studied. On this basis, the mathematical relationship between the color of the projected light source and the color of the corresponding imaging point is proposed; a light source color correction algorithm based on the two-color reflection model is proposed. The object surface color is corrected by three-channel reflectance after the light source color correction algorithm is used for correction. The results suggest that the 3D panoramic vision sensing technology makes the current active stereo vision develop from the visual perception of surface structure to the visual perception of volume structure and reduces the error rate of recognition from 44% to 11%. Moreover, the algorithm does not need to know the coding method of the projected light source and the surface material of the object in advance. The scene teaching combined with technology overcomes the limitations of the teaching site and brings students into a completely realistic teaching situation. The presentation of digital image art using visual sensing technology can inspire children’s imagination, combine education and games, and perform edutainment. Thereby, the application research of digital image art based on 3D panoramic visual sensing technology has an irreplaceable development prospect in the field of art education.

Research on underwater image enhancement algorithm based on improved DCP

All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.

Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.

Leveraging Deep Statistics for Underwater Image Enhancement

Underwater imaging often suffers from color cast and contrast degradation due to range-dependent medium absorption and light scattering. Introducing image statistics as prior has been proved to be an effective solution for underwater image enhancement. However, relative to the modal divergence of light propagation and underwater scenery, the existing methods are limited in representing the inherent statistics of underwater images resulting in color artifacts and haze residuals. To address this problem, this article proposes a convolutional neural network (CNN)-based framework to learn hierarchical statistical features related to color cast and contrast degradation and to leverage them for underwater image enhancement. Specifically, a pixel disruption strategy is first proposed to suppress intrinsic colors’ influence and facilitate modeling a unified statistical representation of underwater image. Then, considering the local variation of depth of field, two parallel sub-networks: Color Correction Network (CC-Net) and Contrast Enhancement Network (CE-Net) are presented. The CC-Net and CE-Net can generate pixel-wise color cast and transmission map and achieve spatial-varied color correction and contrast enhancement. Moreover, to address the issue of insufficient training data, an imaging model-based synthesis method that incorporates pixel disruption strategy is presented to generate underwater patches with global degradation consistency. Quantitative and subjective evaluations demonstrate that our proposed method achieves state-of-the-art performance.