Free Appearance-Editing with Improved Poisson Image Cloning

Bie, Xiaohui; Huang, Haoda; Wang, Wencheng

doi:10.1007/s11390-011-1197-5

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…For example, the stained region in the top left corner of Figure 5a should have been filled with blue color material pixels in Figure 5b but was filled with black color material pixels. Bie et al [32] introduced new energy terms to improve Poisson editing by drawing the specific color strokes and could control the color of the When Poisson editing was directly introduced into stain removal, although it could achieve the purpose of removing the stains since the source image captured fewer stains, it could not restore the original color covered by the stains because of the large color difference, since the target image and source image corresponded to different wavelengths, as shown in Figure 5b. Although the stains could be removed or diluted, the original color is not effectively restored.…”

Section: Feature Band Selectionmentioning

confidence: 99%

Section: Feature Band Selectionmentioning

confidence: 99%

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Virtual Restoration of Stained Chinese Paintings Using Patch-Based Color Constrained Poisson Editing with Selected Hyperspectral Feature Bands

Zhou

Hou

et al. 2019

Remote Sensing

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Stains, as one of most common degradations of paper cultural relics, not only affect paintings’ appearance, but sometimes even cover the text, patterns, and colors contained in the relics. Virtual restorations based on common red–green–blue images (RGB) which remove the degradations and then fill the lacuna regions with the image’s known parts with the inpainting technology could produce a visually plausible result. However, due to the lack of information inside the degradations, they always yield inconsistent structures when stains cover several color materials. To effectively remove the stains and restore the covered original contents of Chinese paintings, a novel method based on Poisson editing is proposed by exploiting the information inside the degradations of selected three feature bands as the auxiliary information to guide the restoration since the selected feature bands captured fewer stains and could expose the covered information. To make the Poisson editing suitable for stain removal, the feature bands were also exploited to search for the optimal patch for the pixels in the stain region, and the searched patch was used to construct the color constraint on the original Poisson editing to ensure the restoration of the original color of paintings. Specifically, this method mainly consists of two steps: feature band selection from hyperspectral data by establishing rules and reconstruction of stain contaminated regions of RGB image with color constrained Poisson editing. Four Chinese paintings (‘Fishing’, ‘Crane and Banana’, ‘the Hui Nationality Painting’, and ‘Lotus Pond and Wild Goose’) with different color materials were used to test the performance of the proposed method. Visual results show that this method can effectively remove or dilute the stains while restoring a painting’s original colors. By comparing values of restored pixels with nonstained pixels (reference of their same color materials), images processed by the proposed method had the lowest average root mean square error (RMSE), normalized absolute error (NAE), and average differences (AD), which indicates that it is an effective method to restore the stains of Chinese paintings.

show abstract

Section: Feature Band Selectionmentioning

confidence: 99%

Section: Feature Band Selectionmentioning

confidence: 99%

Virtual Restoration of Stained Chinese Paintings Using Patch-Based Color Constrained Poisson Editing with Selected Hyperspectral Feature Bands

Zhou

Hou

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Compositing involves merging several images and blending them together in such a fashion that it creates a visually appealing image with beautiful visual effects. Recently, image compositing has become one of the most popular component of digital image editing [1]. The diverse applications of image compositing include photo‐editing [2], advertisement, feature films [3], 3D virtual characters, and panorama generation [4] which make it even more interesting.…”

Section: Introductionmentioning

confidence: 99%

“…Several algorithms use Poisson equation as an effective means for seamless composition. Here, the images are modified by treating the respective gradient fields and finally recovering the composite by solving the Poisson equation [1, 6, 10]. The drawback with these methods is the problem of colour bleeding which changes the colour of the source object, and the object does not look like the original image anymore.…”

Section: Introductionmentioning

confidence: 99%