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Super-resolution inpainting
Abstract
Image or video resources are often received in poor condition, mostly with noise or defects making the resources hard to read. We propose an effective algorithm based on digital image inpainting. The mechanism can be used in restoring images or video frames with very high noise or defect ratio (e.g., 90%). The algorithm is based on the concept of image subdivision and estimation of color variations. Noises inside blocks of different sizes are inpainted with different levels of surrounding information. The results showed that an almost unrecognizable image can be recovered with visually good result. The algorithm can be further extended for processing motion picture with high percentage of noise.
... In order to effectively retain the image data, various researchers have continually proposed various methods of image inpainting [eg. [1][2][3][4][5][6][7][8][9]. These image inpainting methods can be divided into two forms of analysis, which can be viewed from two different perspectives: texture analysis and color analysis. ...
... In the texture analysis, the image inpainting technique considers spatial texture directly up to the related position used [1][2][3][4]. Conversely, in the color analysis, the color compositions of the original image are first converted into various domains through different color system transformations, and then depending on the diverse color composition trend analyses, the color components of damaged regions are repaired separately [5][6][7][8][9]. However, the above mentioned methods are unable to combine their respective advantages in the area of image inpainting in different analysis domains. ...
... First, For prove our image repair method on the highly losing rate image acquire the better repair result than the current image repair method. We comparing our results with shih's image inpainting method [9], we can clearly see that our proposed algorithm generates results that are more enhanced than that of the Shih's method, as illustrated in Table 1. ...
Noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to effectively restore damaged image data, we propose a new image restoration technique based on wavelet transformation analysis. The primary feature of our proposed technique is to initially separate the given image into two principal components which encompass image veins and image color, respectfully. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, a characteristic unique to wavelet transformation, our proposed method processes an image sequentially from the lower frequency layers of an image to the higher frequency layers. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The results were extremely satisfying, even to the extent that the repaired images for distortion to over 90% show little or no indication that the image was once damaged.
... So, it is necessary to obtain the acceleration by utilizing the second derivative of displacements. The response from the stimulation of the structure naturally has turbulence because images or videos are often recorded in poor condition [24]. So, the signal has denoised to level 2, utilizing the Daubechies wavelet transform. ...
Dynamic properties of structures, such as natural frequency, mode shapes, and damping ratios, play a decisive role in the structural behavior against dynamic loads such as earthquakes. One of the utilizations of these properties is the determination of the earthquake forces imposed on structures based on the design spectra. Other applications of these features could be utilized to update the finite element model, detect damage in structures, and long-term health-monitoring of the structures. So, accuracy and reducing the cost of evaluating these properties would have a significant role in increasing the efficiency and the consequences of the useful life of the structures. Image processing is one of the methods for determination of dynamic properties of structures. The aim of this article is presenting a data fusion method to improve the accuracy of extraction of the mode shapes by image processing. For this purpose, a cantilever aluminum beam and a Three-story frame with additional mass have been considered, then their vibration was recorded utilizing the image processing method. To extract dynamic properties of structure, peak survey method was used. The proposed average method in signal level and Improved Distance Evaluation (IDE) in feature level were utilized to improve the accuracy of obtained mode shapes. The accuracy of the novel technique was investigated by comparing the seismic properties to the results from the accelerometer sensors and the finite element method. The results show that this method could enhance the accuracy of the image processing method for remote sensing.
... To this end, it is necessary to obtain the acceleration by utilizing numerical differentiation for the second derivative of displacements which was extracted from the stimulation of the structure (in the first part of image processing). Images or videos are often recorded in poor condition [36], so the response from the stimulation of the structure naturally has turbulence that needs to be corrected. So signal denoising has to be done to make the modal analysis more accurate [37]. ...
Recently, various methods have been developed for remote measurement. The most usage of photogrammetry method is the ability to achieve the whole structure and its efficiency for working in test environments, especially where the use of other measurement methods is not accessible. Although, researchers have utilized this method to measure the vibrations of the structures, the accuracy of image processing methods has always been discussed. This article utilized the close-photogrammetry method for calibration of the commercial camera and also a 2D-projective method used to obtain structural dynamic properties such as mode shapes, natural frequencies, and damping ratios. So, the free vibration of the three-story steel frame has been considered. Then results are compared with other measurement techniques like the results from accelerometer sensors. The comparison of the values from the proposed method with the values obtained from the accelerometer shows that the accuracy of the proposed method has improved significantly.
Currently, noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to restore damaged image data effectively, we propose a novel image inpainting technique based on wavelet transformation. The primary feature of our proposed technique is to separate the given image into two principal components which encompass image texture and color respectively. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The experimental results were perfect, even if the distortion portions of the repaired images were higher than 90%
We present a new image completion method that can deal with large holes surrounded by different types of structure and texture. Our approach is based upon creating image structure in the hole while preserving global image structure, and then creating texture in the hole constrained by this structure. The images are segmented into homogeneous regions. Similar regions touching the hole are linked, resulting in new areas in the hole that are flood-filled and made to match the geometry of the surrounding structure to provide a globally spatially coherent and plausible topology. This reconstructed structure is then used as a constraint for texture synthesis. The contribution of the paper is two-fold. Firstly, we propose an algorithm to link regions around the hole to create topologically consistent structure in the hole, the structure being then made to match that of the rest of the image, using a texture synthesis method. Secondly, we propose a synthesis method akin to simulated annealing that allows global randomness and fine detail that match given examples. This method was developed particularly to create structure (texture in label images) but can also be used for continuous valued images (texture).
The ability to replace missing data in images and video is of key importance to many application fields. The general-purpose algorithm presented here is inspired by texture synthesis techniques but is suited to any complex natural scene and not restricted to stationary patterns. It has the property to be adapted to both still images and image sequences and to incorporate temporal information when available while preserving the simplicity of the algorithm. This method gives very good results in various situations without user intervention. The resulting computational cost is relatively low and corrections are usually produced within seconds.
A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) "texture synthesis" algorithms for generating large image regions from sample textures and 2) "inpainting" techniques for filling in small image gaps. The former has been demonstrated for "textures"--repeating two-dimensional patterns with some stochasticity; the latter focus on linear "structures" which can be thought of as one-dimensional patterns, such as lines and object contours. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. In this paper, the simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm. Computational efficiency is achieved by a block-based sampling process. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm in removing large occluding objects, as well as thin scratches. Robustness with respect to the shape of the manually selected target region is also demonstrated. Our results compare favorably to those obtained by existing techniques.
An algorithm for the simultaneous filling-in of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one of these functions separately with structure and texture filling-in algorithms. The first function used in the decomposition is of bounded variation, representing the underlying image structure, while the second function captures the texture and possible noise. The region of missing information in the bounded variation image is reconstructed using image inpainting algorithms, while the same region in the texture image is filled-in with texture synthesis techniques. The original image is then reconstructed adding back these two sub-images. The novel contribution of this paper is then in the combination of these three previously developed components, image decomposition with inpainting and texture synthesis, which permits the simultaneous use of filling-in algorithms that are suited for different image characteristics. Examples on real images show the advantages of this proposed approach.
We present a new method for the restoration of digitized photographs. Restoration refers to removal of image defects such as scratches and blotches as well as to removal of disturbing objects as, for instance, subtitles, logos, wires, and microphones. Our method combines techniques from texture synthesis and image inpainting, bridging the gap between these two approaches that have recently attracted strong research interest. Combining image inpainting and texture synthesis in a multiresolution approach gives us the best of both worlds and enables us to overcome the limitations of each of those individual approaches. The restored images obtained with our method look plausible in general and surprisingly good in some cases. This is demonstrated for a variety of input images that exhibit different kinds of defects.
We present a new method for completing missing parts caused by the removal of foreground or background elements from an image. Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method iteratively approximates the unknown regions and composites adaptive image fragments into the image. Values of an inverse matte are used to compute a confidence map and a level set that direct an incremental traversal within the unknown area from high to low confidence. In each step, guided by a fast smooth approximation, an image fragment is selected from the most similar and frequent examples. As the selected fragments are composited, their likelihood increases along with the mean confidence of the image, until reaching a complete image. We demonstrate our method by completion of photographs and paintings.