Objective

2

Reconstructing defective images accurately and efficiently has become increasingly important nowadays. With the development of image analysis and recognition

many reconstructed images have been used for feature extraction

and few algorithms can realize accurate and efficient reconstruction effect. This study reconstructs local image regions based on the directional derivative of a field and proposes a stable field model of image local texture to achieve accuracy and reconstruction efficiency. The point source effect function is chosen as the transfer function of the pixel information relationship between the known region and the defect region. However

the designed point source effect function only considered the function of the gradient in the process of energy transfer. The energy transfer value of pixels in the defective region is calculated. The weighted summation is realized by average filtering. Experimental data show that the reconstruction of the edge of the geometry is not fine enough to greatly improve the accuracy of the actual image reconstruction. Given the problem of the accuracy and efficiency of the two-dimensional image reconstruction (or inpainting)

this study designed the transfer function as the core function

proposed a new relative reconstruction algorithm

and mainly introduced the transfer function because this function involved energy transformation.

Method

2

Stationary images can be regarded as a stable energy field because of the stable result of the interaction between the surface and structure of object and light. Several studies have reconstructed defect images based on a stable field and have proven that the reconstruction effects can achieve the desired visual effect and high accuracy rate. Thus

the stable field model is used in this paper to describe the image local region. The energy value of the defect points is almost the same as that of the points in the nearest neighborhood. Thus

considering the value of these points is of great importance. In view of each pixel in the defect region

a reconstruction model considers the pixels in the known region as transmitting energy to each known pixel. During the reconstruction process

the energy is first transmitted into the nearest neighborhood

the transfer function is then constructed

and second-order Taylor expansion is introduced to achieve this process. Finally

the reconstruction is completed by interpolation according to the energy value in the nearest neighbor domain.

Result

2

This study reconstructed defect typical geometric graphs

gray images

and color images by using an algorithm that contains the transfer function and different interpolation methods. The interpolation methods include nearest neighbor interpolation

bilinear interpolation

and cubic convolution interpolation. Reconstruction results obtained by different interpolation methods are different. Compared with studies on reconstructing image local regions based on the directional derivative of a field

the typical curvature-driven diffusion

Bertalmio-Sapiro-Caselles-Ballester method

and total variation reconstruction algorithms

the reconstruction accuracy increased by approximately 6%

10%

15%

and 13% respectively

and the peak signal-to-noise ratio (PSNR) increased by approximately 2

1

3

and 2.5 dB

respectively. The reconstruction of the damaged edges and texture is clearer than that of the relative reconstruction models. Results improved considerably compared with traditional models because the proposed algorithm differs from traditional algorithms in certain aspects. For traditional algorithms

the main research idea is to use the information around the image defect area to transfer the inside of the region through several iterations. Once an iteration is performed

the value of the transfer function is updated to satisfy the visual effect of human visual observation. However

our algorithm does not involve iterations and transfers the energy value only once.

Conclusion

2

An improved algorithm based on the foundation of the image local region's stable field model and the inpainting algorithms based on the stable field is proposed in this study

which investigates the transfer function of two-dimensional image reconstruction and the related reconstruction algorithms and shows the reconstruction of image edge and texture details. To maintain a good visual effect

the proposed method greatly improved reconstruction accuracy and PSNR

especially in the image defect region edge

and performs well in reconstructing texture details. Experimental results show that the proposed algorithm obtains good effects and has universal applicability to different types of images with varying degrees of defect.