A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV col...
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A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.
As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very importa...
As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.
Colourisation is a kind of computer-aided technology which automatically adds colours to greyscale images. This paper presents a scribble-based colourisation method which treats the flat and edge pixels differently. F...
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Although wavefront parallel processing(WPP) proposed in the HEVC standard and various inter frame WPP algorithms can achieved comparatively high parallelism, their scalability for its parallelism is still very limited...
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Although wavefront parallel processing(WPP) proposed in the HEVC standard and various inter frame WPP algorithms can achieved comparatively high parallelism, their scalability for its parallelism is still very limited due to various dependencies introduced in spatial and temporal prediction in HEVC. In this paper, through pixel correlation analysis, establishment of CTU node model, multi-core resource allocation strategy, we proposed an intra/inter-frame joint WPP coding algorithm for multi-core platform which can significantly improve the parallelism, while achieved good results in bit rate, PSNR, and acceleration ratio. Experiments on standard HD test sequence show that the proposed algorithm can lead to up to 2x, 1.3x speed up compared with the original WPP and IWF parallelism.
The performance of wavelet transform based image compressed sensing coding algorithms severely rely on the level of wavelet transform. To this end, this paper investigated the combination of mutil-level wavelet full s...
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The performance of wavelet transform based image compressed sensing coding algorithms severely rely on the level of wavelet transform. To this end, this paper investigated the combination of mutil-level wavelet full sub-band in compressed sensing framework. Firstly, we constructed a full sub-band coefficient sparse vector of mutil-level discrete wavelet transform. Secondly, we designed a weight matrix to improve measurement matrix. Finally, sparsity vector was processed by compressed sensing to get the measured value. Compared with the exiting algorithms, the experimental results of the proposed algorithm show that the PSNRs of reconstructed images is improve up to 1～2 dB under the same objective quality.
This paper presents a fast quality scalable video coding method based on compressed sensing(CS). The proposed method obtained the coding scheme of the enhancement MJU by using the interlayer and spatial correlation an...
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This paper presents a fast quality scalable video coding method based on compressed sensing(CS). The proposed method obtained the coding scheme of the enhancement MJU by using the interlayer and spatial correlation and kept the base layer's coding scheme unchanged. And the part in the enhancement layer which needed to be fine quantified was combined with the compressed sensing theory selectively which based on the sparsity of the signal and the complexity of the reconstruction. In order to satisfy the coding syntax structure of the reference software, the measurement value got by compressed sensing was complemented by 0 s and the flag bit was set to distinguish the special sub-blocks coded by CS. Experimental results show that the proposed algorithm can effectively improve the efficiency of scalable video coding and reduce the computational complexity.
Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle...
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ISBN:
(纸本)9781509028610
Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.
Single image super resolution (SR) aims to estimate high resolution (HR) image from the low resolution (LR) one, and estimating accuracy of HR image gradient is very important for edge directed image SR methods. In th...
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Since the lighting conditions in strong contrast regions between the light and dark cant be estimated accurately by traditional center/surround Retinex algorithm, the over-enhancement and color distortion may exist. I...
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ISBN:
(纸本)9781509028610
Since the lighting conditions in strong contrast regions between the light and dark cant be estimated accurately by traditional center/surround Retinex algorithm, the over-enhancement and color distortion may exist. In view of this, combining with the human visual characteristics, a color image enhancement algorithm based on tone-preserving was proposed. A determination function was added to the bilateral filter to estimate illuminance image more accurately and weaken over-enhancement. According to human visual masking effect, the improved gamma correction was utilized to correct the brightness of illumination image adaptively and the local contrast of reflection image obtained by division was enhanced based on local statistics. Besides, the final enhanced image was obtained by combining illumination image with reflection image, which can make image appear more natural. Compared with other similar algorithms from both subjective and objective aspects, the results show that this method being applied to low-contrast color image enhancement can not only improve image clarity, but reduce color distortion.
Traditional video synopsis methods model the processing into an optimization formula where relations among objects such as collision cost are utilized while entire re-calculation is introduced under each possible temp...
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ISBN:
(纸本)9781509028610
Traditional video synopsis methods model the processing into an optimization formula where relations among objects such as collision cost are utilized while entire re-calculation is introduced under each possible temporal shift. Unlike the pairwise cost optimization, we propose a low-complexity and efficient online synopsis method where each tube is processed independently. Without tubes' comparison, the rearrangement is accomplished by a simple projection strategy and an updating projection matrix which records the newest information of the moving space. Furthermore, buffer and a predefined fitness condition also help to increase spatial and temporal utilization. Experiment results demonstrate that the proposed method is superior to other synopsis methods in the processing speed and temporal consistency.
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