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This paper presents a new method for the reconstruction of images from samples located at non-integer mesh positions. This is a common scenario for many image processing applications such as multi-image super-resolution, frame-rate up-conversion, or virtual view synthesis in multi-camera systems. The proposed method consists of an iterative procedure that employs adaptive denoising in order to reduce...
Recent low-rank based matrix/tensor recovery methods have been widely explored in multispectral images (MSI) denoising. These methods, however, ignore the difference of the intrinsic structure correlation along spatial sparsity, spectral correlation and non-local self-similarity mode. In this paper, we go further by giving a detailed analysis about the rank properties both in matrix and tensor cases,...
A novel and effective framework for the enhancement of low lighting images is proposed in this paper. The novel framework presents an optimized de-haze algorithm on inverted images to enhance the low-dynamic-range images which optimizes the complicated process of computing the parameters A and t(x). The improved gamma correction is used to enhance the image contrast for providing better visual performance...
In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering...
Techniques for partial discharge (PD) processing have been increasing in recent researches, since on-site PD measurement can provide valuable information about electrical equipments and their insulation. A lot of approaches for PD filtering have been presented in the literature, mainly based on classical techniques of signal processing. However, few approaches evaluate or present effective methods...
In view of the assembly process characteristic of non-silicon flat micro parts in MEMS devices, a micro assembly control system is developed based on an established experimental platform. According to the characteristics of the micro parts, micro parts recognition and positioning method is studied, and a new matching algorithm based on shape template is proposed, then an identification module has...
Recognition of complex language objects is a proposition for human-like perception of the surrounding world by machines. The crucial complication is a high complexity of relationships that may arise in the recognition of these objects. This implies existence of a high variety of approaches for perceived image objects recognition. One of the approaches, to which also this paper is devoted, is based...
This paper aims in presenting a thorough comparison of performance and usefulness of de-noising techniques, belonging to scale-space domain. Multi-scale Transform (MST) based image de-noising techniques overcome the limitation of Fourier based methods, as it provides the local and detailed information of non stationary image at different scales, which is necessary for de-noising process. The MST based...
Image denoisig is becoming an essential step for analysis of images which occurs due to the imperfections of sensors or during the transmission of data. A novel algorithm for image denoising is proposed, based on fuzzy logics. Using fuzzy features an algorithm is primed which adaptively removes impulse noises. This algorithm consists of two parts including the detection of noise and the removal of...
Magnitude-only resting-state fMRI data have been largely investigated via independent component analysis (ICA) for exacting spatial maps (SMs) and time courses. However, the native complex-valued fMRI data have rarely been studied. Motivated by the significant improvements achieved by ICA of complex-valued task fMRI data than magnitude-only task fMRI data, we present an efficient method for de-noising...
This paper presents results of our study intended on analysis of visual quality of different types of images subject to denoising. More than 100 experiments with observers have been carried out to assess visual quality of denoised images. Two filters — standard DCT in sliding blocks and known BM3D filter — are considered. The latter filter often provides better visual quality but not sufficiently...
Video noise reduction based on temporal spatial recursive filter isproposed in this paper. In the proposed model the recursive time-weighted average is applied to the areas where the motion has notbeen detected. The new model is able to be adaptive in each areadepending on whether the area is static or movable. More precisely, more noise removal will be done in the static areas, and lessremoval in...
Thermal infrared is an alternative method in determining the thermal distribution in almost everything. Thermal infrared camera captures the raw thermal images. These raw thermal images most likely to have an unwanted entities. To have an accurate setup especially in hot spot detection system, the raw image must be properly processed. By applying the Discrete Wavelet Transform (DWT) in thermal infrared...
This paper presents a novel method for the reconstruction of images from samples located at non-integer positions, called mesh. This is a common scenario for many image processing applications, such as super-resolution, warping or virtual view generation in multi-camera systems. The proposed method relies on a set of initial estimates that are later refined by a new reliability-based content-adaptive...
We propose a fast, local denoising method where the Euclidean curvature of the noisy image is approximated in a regularizing manner and a clean image is reconstructed from this smoothed curvature. User preference tests show that when denoising real photographs with actual noise our method produces results with the same visual quality as the more sophisticated, nonlocal algorithms Non-local Means and...
We propose a novel signal model, based on sparse representations, that captures cross-scale features for visual signals. We show that cross-scale predictive model enables faster solutions to sparse approximation problems. This is achieved by first solving the sparse approximation problem for the downsampled signal and using the support of the solution to constrain the support at the original resolution...
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality...
As an important research area in image analysis and computer vision, fusion of infrared and visible images aims at delivering an effective combination of image information from different sensors. Since the final fused image is the demonstration of fusion process, it should reveal both source images' vital information distinctly. To achieve this purpose, an image fusion method based on multiscale hybrid...
The neuromorphic visual processing framework mimicking the biological vision system offers an alternative process into applying computer vision in everyday environment. With the growing interest for an effective approach for making detection of vulnerable road users for the purpose of safety enhancement, the proposed neuromorphic visual processing was tested on vulnerable road users such as cyclists...
In this research, we focused low illumination video image of “below 1 lux” obtained by normal type video camera, and we have considered the method of image correction for moving-object detection by inter-frame differencing. The method of this research is the combination of gamma correction and denoising as the preparation of inter-frame differencing. In this paper, F-measure was introduced as objective...
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