Smoke detection in video surveillance is very important for early fire detection. A general viewpoint assumes that smoke is a low frequency signal which may smoothen the background. However, some pure-color objects al...
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ISBN:
(纸本)9781479902880
Smoke detection in video surveillance is very important for early fire detection. A general viewpoint assumes that smoke is a low frequency signal which may smoothen the background. However, some pure-color objects also have this characteristic, and smoke also produces high frequency signal because the rich edge information of its contour. In order to solve these problems, an improved smoke detection method with RGB Contrast-image and shape constrain is proposed. In this method, wavelet transformation is implemented based on the RGB Contrast-image to distinguish smoke from other low frequency signals, and the existence of smoke is determined by analyzing the combination of the shape and the energy change of the region. Experimental results show our method outperforms the conventional methods remarkably.
In this paper, some of the most significant image quality indexes are reviewed and compared with a new method for blockness distortion evaluation. The paper begins with a brief survey on classical measures based on nu...
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ISBN:
(纸本)0780339061
In this paper, some of the most significant image quality indexes are reviewed and compared with a new method for blockness distortion evaluation. The paper begins with a brief survey on classical measures based on numerical difference between original and reconstructed image data (e.g., MSE, SNR and PSNR) and advanced methods aiming at considering the perceptive aspects of image degradation (e.g., Hosaka Plots and other methods based on Human visual System properties like Information Content or Perceptual image Distortion). After, four innovative methods for blockness distortion measurement are proposed: two based on DCT analysis, and two on differential Sobel operator. Results on standard pictures confirm the efficiency of the proposed measures.
In this paper, we propose a convolutional neural network (CNN)-based post-processing filter for video compression with multi-scale feature representation. The discrete wavelet transform (DWT) decomposes an image into ...
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ISBN:
(纸本)9781665475921
In this paper, we propose a convolutional neural network (CNN)-based post-processing filter for video compression with multi-scale feature representation. The discrete wavelet transform (DWT) decomposes an image into multi-frequency and multi-directional sub-bands, and can figure out artifacts caused by video compression with multi-scale feature representation. Thus, we combine DWT with CNN and construct two sub-networks: Step-like sub-band network (SLSB) and mixed enhancement network (ME). SLSB takes the wavelet subbands as input, and feeds them into the Res2Net group (R2NG) from high frequency to low frequency. R2NG consists of Res2Net modules and adopts spatial and channel attentions to adaptively enhance features. We combine the high frequency sub-band output with the low frequency sub-band in R2NG to capture multi-scale features. ME uses mixed convolution composed of dilated convolution and standard convolution as the basic block to expand the receptive field without blind spots in dilated convolution and further improve the reconstruction quality. Experimental results demonstrate that the proposed CNN filter achieves average 2.13 %, 2.63 %, 2.99 %, 4.8 %, 3.72 % and 4.5 % BD-rate reductions over VTM 11.0-NNVC anchor for Y channel on A1, A2, B, C, D and E classes of the common test conditions (CTC) in AI, RA and LDP configurations, respectively.
An image is often corrupted by additive gaussian noise during its acquisition and transmission. Denoising has to be performed on these images to retain the signal and to suppress the noise. Denoising can be performed ...
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ISBN:
(纸本)0780386744
An image is often corrupted by additive gaussian noise during its acquisition and transmission. Denoising has to be performed on these images to retain the signal and to suppress the noise. Denoising can be performed by various methods like thresholding, filtering etc. But these methods did not consider the local space scale information of the image. Here a new type of neural network is constructed for noise reduction, where the space scale information of the image is considered. This method gives a good numerical results and also better visual effects. Keywords: Denoising,Discrete Wavelet Transform, Continuous soft thresholding, Least Mean Square rule.
In this work, a novel bit allocation method based on visual attention and distortion sensitivity is developed for JPEG2000. Although, visual attention map for an image can be measured by using well-known saliency map ...
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ISBN:
(纸本)9781509064946
In this work, a novel bit allocation method based on visual attention and distortion sensitivity is developed for JPEG2000. Although, visual attention map for an image can be measured by using well-known saliency map methods, true visual attention map can be obtained by conducting experiments to determine fixation points and their durations. A perception model might turn these duration of fixations into visual attention levels. Besides visual attention, visual distortion sensitivity may guide the bit allocation process effectively. This is because human visual system is more sensitive to the distortion around the edges than the distortion in the complex textured areas. In this work, a novel visual distortion sensitivity method that considers all edges without using a threshold for gradient magnitude and uses local entropy of gradient orientation distribution is proposed. Thus, the visual attention and the distortion sensitivity level of each code-block determine its quantization parameters. Using bit allocation based on the visual attention map provides higher subjective evaluation score than using bit allocation based on the post compression rate-distortion optimization method or on a previously proposed method based on the saliency map. Secondly, it is shown that the use of visual distortion sensitivity allows higher objective evaluation scores to be attained.
Noise reduction has been a traditional problem in imageprocessing. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequenc...
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ISBN:
(纸本)0780370414
Noise reduction has been a traditional problem in imageprocessing. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequency signal details. However, the local space-scale information of the image is not adaptively considered by standard wavelet thresholding methods. In this paper, a new type of thresholding neural networks (TNN) is presented with a new class of smooth nonlinear thresholding functions being the activation function. Unlike the standard soft-thresholding function, these new nonlinear thresholding functions are infinitely differentiable. Then a new nonlinear 2-D space-scale adaptive filtering method based on the wavelet TNN is presented for noise reduction in images. The numerical results indicate that the new method outperforms the Wiener filter and the standard wavelet thresholding denoising method in both peak-signal-to-noise-ratio (PSNR) and visual effect.
In this paper, we propose a new super resolution technique based on the interpolation followed by registering them using iterative back projection (IBP). Low resolution images are being interpolated and then the inter...
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ISBN:
(纸本)9781467355636;9781467355629
In this paper, we propose a new super resolution technique based on the interpolation followed by registering them using iterative back projection (IBP). Low resolution images are being interpolated and then the interpolated images are being registered in order to generate a sharper high resolution image. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results as weil as the visual results show the superiority of the proposed technique over the conventional and state-of-art image super resolution techniques. For Lena's image, the PSNR is 6.52 dB high er than the bicubic interpolation.
Inpainting applications include object removal on images and videos, crack filling, error concealment, texture synthesis, where in this paper, its usage for image coherence and perspective emphasis on video frames in ...
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ISBN:
(纸本)9781538615010
Inpainting applications include object removal on images and videos, crack filling, error concealment, texture synthesis, where in this paper, its usage for image coherence and perspective emphasis on video frames in 2D image-to-video conversion system is analysed. Besides, the performance of different techniques in object removal and image reconstruction is compared using visual experiments and quality metrics.
In this paper, we present a novel image scaling method that employs a mesh model that explicitly represents discontinuities in the image. Our method effectively addresses the problem of preserving the sharpness of edg...
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ISBN:
(纸本)9781538607008
In this paper, we present a novel image scaling method that employs a mesh model that explicitly represents discontinuities in the image. Our method effectively addresses the problem of preserving the sharpness of edges, which has always been a challenge, during image enlargement. We use a constrained Delaunay triangulation to generate the model and an approximating function that is continuous everywhere except across the image edges (i.e., discontinuities). The model is then rasterized using a subdivision-based technique. visual comparisons and quantitative measures show that our method can greatly reduce the blurring artifacts that can arise during image enlargement and produce images that look more pleasant to human observers, compared to the well-known bilinear and bicubic methods.
The demand for human face enhancement in pictures is increasing. This paper describes an effort to utilize state-of-the-art signal processing technologies for the enhancement of the human face in pictures. First, seve...
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ISBN:
(纸本)9781479961399
The demand for human face enhancement in pictures is increasing. This paper describes an effort to utilize state-of-the-art signal processing technologies for the enhancement of the human face in pictures. First, several non-linear filters are examined, and it is demonstrated that the total variation regularization filter (TV filter) shows the remarkably best effect for skin smoothing including the removal of wrinkles, stains, moles, and freckles. The reason is analyzed in detail. Then, super-resolution technology is utilized to enhance the image quality for specific parts of the face, such as the eye line, pupil, eyelashes, and hair. Facial part extraction technology is also utilized for the enhancement of selected face parts. Interestingly, we found that the super-resolution technology not only improves the clarity of the image but also increases the brilliancy in the pupil and hair. The super-resolution technology used in this paper is based on the non-linear filtering method developed for 4K high-definition television.
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