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In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique’s versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.

When detecting surface defects in a complex industrial cutting environment, the defects are easily polluted and covered by interfering factors (chips or coolant residues). The defect of the surface images with interference factors is a novel problem in the existing studies, and it is also a difficulty in the detection field. Hence, this paper proposes a high-precision anti-interference detection method for surface defects under the influence of complex environment. The detection method provides a new research idea, which is divided into three main processes: interference regions location, interference regions repair, defect detection. The regions affected by interference factors are adaptively located through the proposed Efficient Channel Attention Network (ECANet)-DeeplabV3 + network model. The mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) of ECANet-DeeplabV3 + network model for interference factor identification are 98.37% and 95.46%, respectively. The Criminisi algorithm is improved from priority, finding the best matching block, and searching regions. Directional repair based on the improved Criminisi algorithm is performed on the identified interfering regions removing the interfering factors in the image, which is the research core. Then, defect detection is performed on the repaired image using the improved superpixel technology. At the same time, the defect detection results provide a variety of surface defect information for the cutting staff, including defect types, the number of pixels in different defect regions, and the area ratio of different defect regions. This information improves predictive maintenance and surface quality control.

As a robust digital image inpainting technology, the Criminisi algorithm (CA) has been widely used. However, its high running time that it needs to search in the entire undamaged area of the image to determine an optimal matching block presents a challenge. To address this issue, this study proposes an improved version of CA, named PSO-CA, which incorporates the particle swarm optimization algorithm (PSO) with CA. The running time of the CA is significantly reduced benefiting from the parallel optimization capability of the PSO. In addition, the search space is restricted to the neighbouring region of the block that needs to be filled. The availability of the proposed PSO-CA algorithm is assessed in the laboratory colour model by the running time and three matching indices, such as the peak signal-to-noise ratio (PSNR). The experimental results indicate that PSO-CA significantly enhances the inpainting speed and produces the same or better results compared with the initial CA and the Criminisi with search space algorithm (CWSS).

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.

Preserving heritage paintings across the globe has nowadays gained momentum to let know artistic values of our ancestors in terms of their art techniques and natural material used in creating variety of magnificent paintings so that remains as witness and evidences of ancient historical and cultural heritage. Also, reconstruction of degraded medical images for proper diagnosis is crucial concern for the medical industry. An efficient texture-structure conserving patch matching algorithm (TSCPMA) has been proposed to inpaint the degraded region of an image. The novel feature of Criminisi algorithm to generate large missing areas and reconstruct small gaps is enhanced by improving quality of inpainting and removing existing drawbacks. The priority dependency on confidence and data had been removed by selecting patch to reconstruct with least number of unknown elements. The criteria for minimum similarity distance to select the best patch match had been refined for better patch match thus improving inpainting quality. The target pixel is assigned final value after all unknown pixels from the degraded region have been estimated. The look up table is updated at each iteration so that neighbourhood information can better relate adjacent pixels rather than approximating them with values from other distant known regions of the image. The proposed TSCPMA is able to preserve the color, textural and structural quality of the reconstructed patches as indicated by inpainted results and performance parameters when compared with state of art methods.

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.

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.

Cultural relic is the carrier of human historic culture, which can reflect the cultural and social environment, but cultural relics as a material will be damaged over time. Before the advent of computer technology, the damaged cultural relics would not be repaired due to cost. Computer vision technology has been applied to the restoration of cultural relics, mainly for the virtual restoration of damaged cultural relics images. This paper briefly introduced the Criminisi image restoration algorithm and the structure tensor used to improve the algorithm in the digital cultural relics image restoration. A damaged cultural relics image and a complete image which was damaged by human were repaired respectively using the classical Criminisi image restoration algorithm and the improved structure tensor based repair algorithm on MATLAB software. The results showed that the Criminisi image restoration algorithm could be used to repair the damaged images of ancient fabrics. It was found that the classical image restoration algorithm had some shortcomings, such as inappropriate texture structure, obvious repair marks and addition of redundant information, but the improved algorithm effectively avoided the above shortcomings. The peak signal to noise ratio (SNR) of the complete image which was damaged by human was compared objectively, and it was found that the improved algorithm had better restoration performance.

AI intelligent detection of colon polyp has been found as a highly popular research direction. Moreover, mainstream research places a focus on how to recognize colon polyp using a better neural network model architecture. The video employed for recognition will be considered the original video output by the endoscope. Through research, it was found that besides the prominent neural network architecture, more excellent video preprocessing algorithms can significantly increase the accuracy of recognition and location for colon polyp. As revealed by the research result, the relative highlight area attributed to uneven illumination and the absolute highlight area attributed to specular reflection are the main factors of the recognition of colon polyp by the neural network. To solve the problem above, all highlight areas are divided into four categories, i.e., the relative highlight area, the large absolute highlight area, the medium absolute highlight area and the small absolute highlight area. This study designs different restoration algorithms in accordance with the nature and characteristics of the respective categories. The relative highlight area can be corrected and restored using the two-dimensional (2d) gamma function. The large absolute highlight area will not be processed since it will not reduce the recognition accuracy of the neural network. The small absolute highlight area has a slight effect on the recognition accuracy of the neural network, so the surrounding color filling method will be adopted to restore the area. The medium absolute highlight area will be restored by the optimized Criminisi algorithm. The test is performed on four neural networks, i.e., the Unet, Unet++, ResUnet and ResUnet++. After the sample is processed by this algorithm, the results show that the recognition accuracy of colon polyps by four kinds of neural network is significantly improved. Compared with other image restoration algorithms that take tens of seconds, the image restoration algorithm in this study takes less than 90 ms, which obviously reduces the time, and can basically meet the real-time requirements of AI intelligent detection.

In patch-based inpainting methods, the order of filling the areas to be restored is very important. This filling order is defined by a priority function that integrates two parameters: confidence term and data term. The priority, as initially defined, is negatively affected by the mutual influence of confidence and data terms. In addition, the rapid decrease to zero of confidence term leads the numerical instability of algorithms. Finally, the data term depends only on the central pixel of the patch, without taking into account the influence of neighboring pixels. Our aim in this paper is to propose an algorithm to solve the problems mentioned above. This algorithm is based on a new definition of the priority function, a calculation of the average data term obtained from the elementary data terms in a patch and an update of the confidence term slowing its decrease and avoiding convergence to zero. We evaluated our method by comparing it with algorithms in the literature. The results show that our method provides better results both visually and in terms of the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM).