In this paper, we proposed the adaptive inpainting method according to the multi-resolution of nearing damaged district of human visual characteristics. We explore the impacts of these techniques of the decomposition, the priority in decision-marking and repair techniques on result of image inpainting. Form this, we proposed the four methods for restoration of damaged images.
1. Progressive image inpainting: The digital image inpainting based on wavelet transform. This is, using the two-level wavelet transform to decomposition the image into three wavelet layers of different frequency components (low, middle and high) to carry on image inpainting procedure. First, contour estimation with coarse resolution is conducted on the low frequency wavelet layer, and the image is repaired according to the obtained coarse contour. Based on the repairing results, the wavelet layers are progressively repaired, gradually moving from lower to higher frequencies to carry out finer texture repair and producing results that are more consistent with the human visual perception.
2. Adaptive decomposition inpainting: In order to resolve the issue of false repair results at sites with big damage district, we propose to perform adaptive decomposition of wavelet transform. The size and extent of the damaged region are evaluated to obtain the corresponding wavelet layers for carrying out adaptive decomposition of the image. By examining the similarity in contour and texture in the same image, self-similarity decision-making rules are then proposed to conduct image repair.
3. Geometric Bandelet Inpainting: Although wavelet transform allows decomposition of an image into different resolution layers, it cannot achieve perfect decomposition on two-dimensional images. Therefore, if repair is conducted directly using the wavelet coefficients, the resulting image will not achieve the desirable refined quality. To overcome this and to acquire satisfactory image repair results, we propose to carry out image repair by taking advantage of the concept of bandelet transform, as well as the geometric flow of image contour and texture.
4. Watermark Inpainting: In the case when the damaged region contains different multiple objects, the limited contour information will not allow image repair to be carried out correctly. To solve this problem, the image contour watermark previously embedded in the image is used as a reference to guide the image repair work. Thus, this method for repairing damaged images is based on the analysis of image watermark.
In this thesis, we investigated restoration of damaged images using the four kinds of methods described above. The four methods are distinguished by their applicability to damaged regions of various sizes and textures. From our experimental results, we discovered that we can successfully decompose an image with large-scale damage into different resolution layers and even adaptively decompose the image according to the size and extent of damage. We were able to obtain sufficient number of image analysis layers and reduce the complexity of information in each layer to enable effective and progressive repairing on damaged images. In addition, by using bandelet transform, we were able to adaptively decompose damaged images according to the trend in their contours and textures, making the distribution of coefficients in each layer more concentrated and allowing finer repair results to be obtained. We also found that we can significantly increase the reconstructability of damaged images if the contour watermark of the original image is used as a reference for conducting image repair.