摘要 :
In this paper, a hybrid filter based on the concept of fractional calculus and Alexander polynomial is proposed. The hybrid filtering mask is constructed by convolving the designed Alexander fractional differential and integral ma...
展开
In this paper, a hybrid filter based on the concept of fractional calculus and Alexander polynomial is proposed. The hybrid filtering mask is constructed by convolving the designed Alexander fractional differential and integral masks. The hybrid mask shows high robustness for images corrupted with Gaussian, salt & pepper, and speckle noises. For the experimentation, the standard and real world noisy images are used. The qualitative comparison shows that the proposed hybrid filter has better denoising with high edge preserving capability as compared to the other existing filters. Quantitatively, the performance of the proposed hybrid filter is also evaluated by the measures such as peak signal to noise ratio (PSNR), normalized cross-correlation (NK), average difference (AD), structural content (SC), maximum difference (MD) and normalized absolute error (NAE) on standard set of images. The average values of these metrics for Gaussian noise with maximum standard deviation σ=25 are PSNR = 32.729, NK = 0.8190, AD = 0.01825, SC = 0.8527, MD = 87, NAE = 0.0637. The experimentation reveals that the proposed hybrid filter gives better improvement as compared with other existing filters both qualitatively and quantitatively.
收起
摘要 :
This paper aims to explore frequency behavior of isotropic (regular SIFT) and anisotropic (Bi-SIFT and Tri-SIFT) versions of the scale-space keypoint detection algorithm SIFT. We introduced a new smoothing function Trilateral filt...
展开
This paper aims to explore frequency behavior of isotropic (regular SIFT) and anisotropic (Bi-SIFT and Tri-SIFT) versions of the scale-space keypoint detection algorithm SIFT. We introduced a new smoothing function Trilateral filter that can be used in formation of a scale-space as an alternative to the Gaussian scale-space. The number of matching pixels, warping error, and scatteredness are employed in comparison. We made the comparison out of face dataset and object dataset for scale, orientation, and view-angle transformations as well as lighting and compression variations. The comparison results show that anisotropic smoothing detects more keypoints than isotropic one. The Tri-SIFT is more robust to variation in viewpoint angle.
收起
摘要 :
The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have be...
展开
The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have been proposed. Unfortunately, little is known about the accuracy of such accelerations. In this paper, we propose a new signal-processing analysis of the bilateral filter which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity is added to the original domain dimensions. Importantly, this signal-processing perspective allows us to develop a novel bilateral filtering acceleration using down-sampling in space and intensity. This affords a principled expression of accuracy in terms of bandwidth and sampling. The bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities. This allows us to derive criteria for downsampling the key operations and_ achieving important acceleration of the bilateral filter. We show that, for the same running time, our method is more accurate than previous acceleration techniques. Typically, we are able to process a 2 megapixel image using our acceleration technique in less than a second, and have the result be visually similar to the exact computation that takes several tens of minutes. The acceleration is most effective with large spatial kernels. Furthermore, this approach extends naturally to color images and cross bilateral filtering.
收起
摘要 :
Existing image fusion methods focus on aggregate image features from different modalities into a clear and comprehensive image. However, these solutions ignore the importance of gradient features, which results in smooth performan...
展开
Existing image fusion methods focus on aggregate image features from different modalities into a clear and comprehensive image. However, these solutions ignore the importance of gradient features, which results in smooth performance of contrast information in the fused images. In this paper, an edge gradient enhancement method for infrared and visible image fusion is proposed, named EgeFusion. First, the source images are decomposed into a series of base and detail layers through a simple weighted least squares filter. Next, sub-window variance filter is proposed for the fusion of detail layers. For the base layer, a fusion strategy that combines visual saliency mapping with the idea of adaptive weight assignment is designed. The method effectively assigns the saliency features in source images globally, thus providing more comprehensive and valuable information about the region of interest in fused images. Finally, the fused base and detail layers are reconstructed in reverse to obtain the fusion results. The experimental results show that the proposed method has significantly enhanced the gradient features in source images, which makes it easier for the human eye system to focus on the region of interest. Compared with other state-of-the-art fusion methods, the proposed EgeFusion has superior visual quality and acceptable results in infrared and visible, multi-focus, as well as multi-modal medical image fusion. More importantly, our approach achieves performance improvements on object detection.
收起
摘要 :
In this paper, The effect Daubechies Wavelet Filter for enhancement objects Blur images in an environment containing noise are presented. The idea of filtering is that the Daubechies Wavelet Filter to enhancement objects with any ...
展开
In this paper, The effect Daubechies Wavelet Filter for enhancement objects Blur images in an environment containing noise are presented. The idea of filtering is that the Daubechies Wavelet Filter to enhancement objects with any type of noise and any noise facter. The results show that Daubechies Wavelet Filter is more efficient than other methods in enhancement. Environment poecess for paper is MATLAB data set are 20 Blur images with any format).
收起
摘要 :
0.05. Conclusion: This study reveals that for the medical image enhancement purpose the novel median filter provides better PSNR than the linear contrast algorithm on ultrasound liver images.
摘要 :
A fast adaptive and selective mean filter is presented to remove salt and pepper noise effectively from images corrupted with higher noise densities. The algorithm achieves better results in terms of visual quality and in terms of...
展开
A fast adaptive and selective mean filter is presented to remove salt and pepper noise effectively from images corrupted with higher noise densities. The algorithm achieves better results in terms of visual quality and in terms of peak signal-to-noise ratio, mean absolute error, mean structural similarity index measure, image enhancement factor, and edge preservation ratio than many existing state-of-the-art algorithms at all noise densities. Adaptive filters that use variable window size produce better restoration of salt and pepper noise at higher noise densities than filters that use fixed window size, but they consume more time. This makes them practically impossible to implement them in digital image acquisition devices. Hence, reducing the execution time of adaptive filters is vital. The proposed algorithm consumes around 90\% less time for lower noise densities and 50\% less time for higher noise densities than the adaptive weighted mean filter, one of the best available adaptive filters in the literature for high-density salt and pepper noise removal.
收起
摘要 :
Filtering images is required by numerous multimedia, computer vision and graphics tasks. Despite diverse goals of different tasks, making effective rules is key to the filtering performance. Linear translation-invariant filters wi...
展开
Filtering images is required by numerous multimedia, computer vision and graphics tasks. Despite diverse goals of different tasks, making effective rules is key to the filtering performance. Linear translation-invariant filters with manually designed kernels have been widely used. However, their performance suffers from content-blindness. To mitigate the content-blindness, a family of filters, called joint/guided filters, have attracted a great amount of attention from the community. The main drawback of most joint/guided filters comes from the ignorance of structural inconsistency between the reference and target signals like color, infrared, and depth images captured under different conditions. Simply adopting such guidelines very likely leads to unsatisfactory results. To address the above issues, this paper designs a simple yet effective filter, named mutually guided image filter (muGIF), which jointly preserves mutual structures, avoids misleading from inconsistent structures and smooths flat regions. The proposed muGIF is very flexible, which can work in various modes including dynamic only (self-guided), static/dynamic (reference-guided) and dynamic/dynamic (mutually guided) modes. Although the objective of muGIF is in nature non-convex, by subtly decomposing the objective, we can solve it effectively and efficiently. The advantages of muGIF in effectiveness and flexibility are demonstrated over other state-of-the-art alternatives on a variety of applications. Our code is publicly available at https://sites.google.com/view/xjguo/mugif.
收起
摘要 :
In this study, the median-based edge-preserving (EP) modification is revisited and has been shown to improve effectively the quality of a reconstructed Bayer colour filter array image in terms of the composite peak signal-to-noise...
展开
In this study, the median-based edge-preserving (EP) modification is revisited and has been shown to improve effectively the quality of a reconstructed Bayer colour filter array image in terms of the composite peak signal-to-noise ratio (CPSNR) performance index. Along this research direction, the authors propose an EP-modified signal-correlation-based (SCB) post-processing (PP), called EP-SCB, as an enhancement to any existing interpolation method. The Bayer image reconstruction system they consider thus consists of two operational phases. The first phase performs an initial estimation of the missing red, green and blue colours by using an existing interpolation method, whereas the second phase applies EP-SCB PP. Since a certain class of images may not fulfil the premise of having small variations in local colour difference, which is assumed by SCB-type interpolation, thereby resulting in a deterioration in CPSNR after EP-SCB PP, a threshold test on local variance ratio is also devised to conditionally switch off the second phase. Experimental results show that the EP-SCB PP with variance-ratio test gives a worst average CPSNR than the original interpolation methods tested in none of the Kodak and IMAX image groups experimented.
收起
摘要 :
Image diffusion can smooth away noise and small-scale structures while retaining important features, thus improving the performances for many image processing algorithms. In this paper, we present a novel diffusion algorithm for w...
展开
Image diffusion can smooth away noise and small-scale structures while retaining important features, thus improving the performances for many image processing algorithms. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries. The effectiveness of the proposed approach is validated by experiments on various medical images.
收起
原文获取