Image Repair Strategies in the Pricey Prawn Scandal of Qingdao

The pricey prawn scandal of Qingdao in the 2015 National Day Holiday,not only violates the principle of integrity management,but also seriously damages the image of Qingdao—World Famous Tourist City.This study applies image repair strategies to Qingdao municipal government departments' attempt to restore its damaged image in crisis communication.Through a discourse analysis,it is found that the government departments mainly adopt the following strategies: denial,evasion of responsibility(defeasibility),reduction of offensiveness(bolstering,compensation),corrective action and mortification.Then we evaluate the effectiveness of this image repair effort.

“To measure is to know”: how advances in image analysis are supporting neural repair strategies

Neuronal regeneration in the peripheral nervous system arises via a synergistic interplay of neurotrophic factors,integrins,cytoskeletal proteins,mechanical cues,cytokines,stem cells,glial cells and astrocytes.

Incomplete Image Completion through GAN

There are two difficult in the existing image restoration methods.One is that the method is difficult to repair the image with a large damaged,the other is the result of image completion is not good and the speed is slow.With the development and application of deep learning,the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data.In the process of image completion,the first step is trained the generator to simulate data distribution and generate samples.Then a large number of falsified images are quickly generated using the generative adversarial network and search for the code of the closest damaged image.Finally,the generator generates missing content by using this code.On this basis,this paper combines the semantic loss function and the perceptual loss function.Experimental result show that the method successfully predicts the information of large areas missing in the image,and realizes the photorealism,producing clearer and more consistent results than previous methods.

Design of axial flaps with color Doppler flow imaging technique for repairing deep wounds of heels

To report the methods and effect of axial pattern flap on lower limb in repairing deep wounds of heels by using color Doppler flow imaging (CDFI) technique so as to solve the ever before problems that the vessel can not be displayed in designing axial flap.Methods Suitable axial flaps on lower limbs were selected according to the character of the wounds.There were 25 flaps including 10 cases of the distal-based sural neurovascular flap,nine medial sole flap and six medial leg flap.All the axial pattern flaps were designed on the basis of traditional design ways before operation;then,CDFI appliance with high resolution was used to examine the starting spot,exterior diameter,trail and length of the flap’s major artery.The flaps were redesigned according to the results of CDFI and transferred to cover the wounds.In the meantime,both the results of operation and examination were compared.Results The major artery’s starting spot,exterior diameter,trail and anatomic layers were displayed clearly,in consistency with the results of operation.The flaps survived completely and recovered well,with perfect appearance,color and arthral function.Conclusion CDFI is a simple,macroscopic and atraumatic method for designing the axial pattern flap on lower limb,can provide more scientific and accurate evidence for preoperative determination of flap transplantation and is worthy of clinical application.10 refs,4 figs,2 tabs.

Repair approach for DMC images based on hierarchical location using edge curve

The color composite digital mapping camera （DMC） images are produced by the post-processing software of Z/I imaging. But the failure of radiometric correction in post-processing leads to residual radiometric differences between CCD images, which then affect the quality of the images in further applications. This paper, via analyzing the characters and causes of such a phenomenon, proposes a repair approach based on hierarchical location using edge curve. The approach employs a hierarchical strategy to locate the transition area and seam-line automatically and then repair the image through the global reconstruction between CCD images and the local reconstruction in the transition area. Experiments indicate that the approach proposed by this paper is feasible and can improve the quality of images effectively.

Texture Repairing by Unified Low Rank Optimization

In this paper, we show how to harness both low-rank and sparse structures in regular or near-regular textures for image completion. Our method is based on a unified formulation for both random and contiguous corruption. In addition to the low rank property of texture, the algorithm also uses the sparse assumption of the natural image： because the natural image is pieeewise smooth, it is sparse in certain transformed domain （such as Fourier or wavelet transform）. We combine low-rank and sparsity properties of the texture image together in the proposed algorithm. Our algorithm based on convex optimization can automatically and correctly repair the global structure of a corrupted texture, even without precise information about the regions to be completed. This algorithm integrates texture rectification and repairing into one optimization problem. Through extensive simulations, we show our method can complete and repair textures corrupted by errors with both random and contiguous supports better than existing low-rank matrix recovery methods. Our method demonstrates significant advantage over local patch based texture synthesis techniques in dealing with large corruption, non-uniform texture, and large perspective deformation.

DIUN:Deeper Inception U-Network for Recovering Partial Pixelated Images

In our daily life,it is nothing strange to see pixelated images that are spoiled artificially to hide certain information for protecting privacy or pixelated deliberately to cover up bad behaviors even crimes.To prevent these phenomena and recover the true information from pixelated images,it is meaningful to research an effective reconstruction method for recovering pixelated images.This paper aims at recovering the artificial partial pixelated images via deep learning(DL).To abstract more abundant features and enhance the repair ability of DL model,we propose a new DL structure,called deeper inception U-Net,to act as the generator of a generative adversarial network.We combine the feature loss with structural similarity index measure loss as the context loss to minimize the distance between feature maps of clear images and the generated images,which helps to improve the quality of repair images.After obtaining inception features,we use fusion layer to adaptively learn featuresin each inception block.To evaluate the performance of our model,we introduce a new home dataset that contains 10174 clear home images with corresponding pixelated images.A series of experiments show that our model has ability to rebuild pixelated images.

A Novel Variational Image Model： Towards a Unified Approach to Image Editing

In this paper we propose a unified variational image editing model. It interprets image editing as a variational problem concerning the adaptive adjustments to the zero- and first-derivatives of the images which correspond to the color and gradient items. By varying the definition domain of each of the two items as well as applying diverse operators, the new model is capable of tackling a variety of image editing tasks. It achieves visually better seamless image cloning effects than existing approaches. It also induces a new and efficient solution to adjusting the color of an image interactively and locally. Other image editing tasks such as stylized processing, local illumination enhancement and image sharpening, can be accomplished within the unified variational framework. Experimental results verify the high flexibility and efficiency of the proposed model.