Search Results (5)

The digital grating focusing and leveling sensor is a kind of wafer height sensor for focus control in a lithography tool by measuring the displacement of an optical grating image reflected from the wafer surface. The process pattern on the wafer surface can significantly affect the measurement accuracy of the sensor. To mitigate this effect, the Criminisi algorithm for image processing is employed. First, process patterns in the optical grating image are identified and masked with a specific color—yellow in this paper. The Criminisi algorithm is then applied to recover the clear image in the masked region. To evaluate the algorithm performance, 50 masked images are recovered and compared with the original clear image where the mask ratios range from 1% to 15%. The experimental results indicate that the mean repair accuracy is below 1 nm after 10 repair iterations for a given mask ratio and the maximum error in a single repair is 68 nm across all 50 images. Full article

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results. Full article

Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the original first principal component with a high-pass filtered first principal component image, which was then inverse PCA transformed with the other original principal component images to obtain an enhanced hyperspectral image. The linear information in the mural was therefore enhanced, and the differences between the scratches and background improved. Second, the enhanced hyperspectral image of the mural was synthesized as a true colour image and converted to the HSV colour space. The light brightness component of the image was estimated using the multi-scale Gaussian function and corrected with a 2D gamma function, thus solving the problem of localised darkness in the murals. Finally, the enhanced mural images were applied as input to the triplet domain translation network pretrained model. The local branches in the translation network perform overall noise smoothing and colour recovery of the mural, while the partial nonlocal block is used to extract the information from the scratches. The mapping process was learned in the hidden space for virtual removal of the scratches. In addition, we added a Butterworth high-pass filter at the end of the network to generate the final restoration result of the mural with a clearer visual effect and richer high-frequency information. We verified and validated these methods for murals in the Baoguang Hall of Qutan Temple. The results show that the proposed method outperforms the restoration results of the total variation (TV) model, curvature-driven diffusion (CDD) model, and Criminisi algorithm. Moreover, the proposed combined method produces better recovery results and improves the visual richness, readability, and artistic expression of the murals compared with direct recovery using a triple domain translation network. Full article

Based on current manual restoration methods, a better algorithm for restoring images based on sample blocks is proposed, along with a sustainable restoration technique for digital virtualization, with the aim of preserving and restoring the priceless art of ancient architectural motifs. The paper uses curve fitting to pre-process the restored photos by re-constructing their damaged borders and filling in the structural information that is absent with the aid of an enhancement of the Criminisi method. The repaired photos have improved edges that were previously blurry, fractured, and over-extended. In order to increase the dependability of the priority calculation when restoring photos and make it possible to acquire a more precise restoration order, we rewrote the priority calculation formula for restoration blocks in the Criminisi algorithm. The purpose was to enhance the aesthetics of the photographs and provide a viable and sustainable restoration technique for the restoration of ancient architectural motifs in Fujian. The Criminisi algorithm with deep learning is used in the thesis to fully restore the content, color, and texture of the architectural photographs, bringing the murals as close to their original state as is practical. In order to improve the blurry, broken, and over-extended edges of the restored images, the broken edges of the images are first repaired through image pre-processing. Then, adjustment factors are added to the priority calculation to increase the weight of the data items, resulting in a more accurate priority order while preventing the priority values from degrading quickly in the later stages of restoration. The PSNR values of the restored images were calculated and compared to those of the Criminisi method, demonstrating that the revised algorithm produces better restoration results and can effectively improve restoration efficiency while lowering restoration costs and ensuring pattern restoration sustainability. By retaining as much of the structural information of the original image as possible in the design of the network model and allocating larger weights to the structural part, this process also uses style migration in deep learning to restore the texture and color of the mural. As a result, the final image is as similar to the original image as possible in terms of content and as similar as possible to the style image in terms of color and texture. A better solution is proposed based on the Criminisi algorithm. By comparing the experimental results of the three sets of building images, the PSNR values of the priority improvement algorithm (30.26, 38.06, 39.56) were significantly better than those of the Criminisi algorithm (27.59, 37.06, 37.59), using the peak signal-to-noise ratio (PSNR) values as a reference standard. In order to determine the appropriate restoration sequence and enhance the quality of picture repair, the broken edges of the pattern are strengthened. The algorithm’s matching criteria can be applied in subsequent work to improve sample-matching accuracy and produce better sustainable restoration results for ancient architectural patterns in Fujian. It no longer requires specialized professional knowledge to reproduce the color of faded architectural photos; instead, a style migration approach is employed to recover the color and texture of architectural images. This study proposes the use of a texture synthesis method and a layered processing method through which the PSNR values of the resulting restored images calculated are superior and significantly higher than those of the sample-based method and the variational framework of synthetic images with regular texture components. We achieved the creation of an updated Criminisi algorithm-based solution that improves the quality of image restoration by fortifying the pattern’s frayed edges and determining the optimum repair order. These two techniques can be combined to improve the sustainability of restoration of faded architectural photographs for issues such as pattern breakage, color loss and fading. To achieve better restoration results for the historic architectural patterns in Fujian, the accuracy of sample matching can be increased, starting with the algorithm’s matching criterion. Full article

Hyperspectral imaging is a non-destructive method for the detection of chilling injury in fruit. However, the limitation of this technique is the lacking of an appropriate working parameters and a feasible discriminating model for chilling on-line sorting. This research was aimed to select the optimal spectral resolution, scanning speed, and classification model for green jujube chilling injury detection based on hyperspectral reflectance imaging. Criminisi algorithm was firstly carried out to reconstruct the specular reflection region in spectral images before deriving mean spectra, and thus the optimal wavelengths were selected by random frog. Results showed that the Criminisi algorithm presented a desirable ability of spectral image inpainting. The linear discriminant analysis (LDA) achieved overall accuracies of 98.3% and 93.3% for two-class and three-class classification, respectively, at the speed of 20 mm/s with the spectral resolution of 5.03 nm based on selected spectral features. The results demonstrated that 20 mm/s with the spectral resolution of 5.03 nm was more feasible for the detection of green jujube chilling injury in hyperspectral imaging system due to a higher scanning efficiency, but a less data size. Full article