“Admiration of Flowers in Poems”-On Image Rendering of Flowers in Tang Poems Under the Guidance of Green Translation

The progress of economy and social development is the driving force of translation research and practice.With the deepening of global development and cooperation,global ecology and green concept has become a topic of concern and is capturing increasing worldwide attention.Under this circumstances,green development has become the key to building global ecological civilization.“Strengthening ecological consciousness and green consciousness,shouldering ecological responsibility”has also become the main theme of the times.In response to this green trend,Green Translation lays stress on the green concept,green behaviour,and the green value in translation practice.The flower images,which take up a large proportion of plant images in Tang poetry,are of high ecological value and significance.This paper takes the Translation of 300 Tang Poems as the research subject,conducts a study on the image rendering of flowers in Tang poetry from the three aspects of Green Translation:language ecology,cultural ecology and communicative ecology.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Color Estimation for Thermal Infrared Imagery Based on Kernel PCA and Sparse Representation

Adding colors to monochrome thermal infrared images can help observers understand the scenery better. A nonlinear color estimation method for single-band thermal infrared imagery based on kernel principal component analysis (KPCA) and sparse representation was proposed. Nonlinear features of infrared image were extracted using KPCA. The relationship between image features and chromatic values was learned using sparse representation and a color estimation model was obtained. The thermal infrared images can be rendered automatically using the color estimation model. The experimental results show that the proposed method can render infrared image with an accurate color appearance. The proposed idea can also be used in other color estimation problem.

An adaptive interpolation algorithm for hole-filling in free viewpoint video

Depth image based rendering(DIBR) is an effective approach for virtual view synthesis in free viewpoint television and 3D video.One of the important steps in DIBR is filling the holes caused by disocclusion regions and wrong depth values.Most of the existing hole-filling methods work well in areas of low spatial activity but fail to obtain satisfactory results in high spatial activity regions.In this paper,we combine the depth based hole-filling and the adaptive recursive interpolation algorithm which is capable of handling edges passing through the missing areas.Accoring to the experimental results,we confirm that the depth based adaptive recursive interpolation algorithm can provide better rendering quality objectively and subjectively.