In this paper, two new end-to-end image compression architectures based on convolutional neural networks are presented. The proposed networks employ 2D wavelet decomposition as a preprocessing step before training and...
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ISBN:
(数字)9781510629684
ISBN:
(纸本)9781510629684
In this paper, two new end-to-end image compression architectures based on convolutional neural networks are presented. The proposed networks employ 2D wavelet decomposition as a preprocessing step before training and extract features for compression from wavelet coefficients. Training is performed end-to-end and multiple models operating at different rate points are generated by using a regularizer in the loss function. Results show that the proposed methods outperform JPEG compression, reduce blocking and blurring artifacts, and preserve more details in the images especially at low bitrates.
In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The feat...
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In long range imagery, the atmosphere along the line of sight can result in unwanted visual effects. Random variations in the refractive index of the air causes light to shift and distort. When captured by a camera, t...
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ISBN:
(纸本)9781510639003
In long range imagery, the atmosphere along the line of sight can result in unwanted visual effects. Random variations in the refractive index of the air causes light to shift and distort. When captured by a camera, this randomly induced variation results in blurred and spatially distorted images. The removal of such effects is greatly desired. Many traditional methods are able to reduce the effects of turbulence within images, however they require complex optimisation procedures or have large computational complexity. The use of deep learning for imageprocessing has now become commonplace, with neural networks being able to outperform traditional methods in many fields. This paper presents an evaluation of various deep learning architectures on the task of turbulence mitigation. The core disadvantage of deep learning is the dependence on a large quantity of relevant data. For the task of turbulence mitigation, real life data is difficult to obtain, as a clean undistorted image is not always obtainable. Turbulent images were therefore generated with the use of a turbulence simulator. This was able to accurately represent atmospheric conditions and apply the resulting spatial distortions onto clean images. This paper provides a comparison between current state of the art image reconstruction convolutional neural networks. Each network is trained on simulated turbulence data. They are then assessed on a series of test images. It is shown that the networks are unable to provide high quality output images. However, they are shown to be able to reduce the effects of spatial warping within the test images. This paper provides critical analysis into the effectiveness of the application of deep learning. It is shown that deep learning has potential in this field, and can be used to make further improvements in the future.
imageprocessing is emerging research area which seeks attention in biomedical field. There are lots of imageprocessing techniques which are not only useful in extracting useful information for analysis purpose but a...
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ISBN:
(纸本)9789811331404;9789811331398
imageprocessing is emerging research area which seeks attention in biomedical field. There are lots of imageprocessing techniques which are not only useful in extracting useful information for analysis purpose but also saves computation time and memory space. Transformation is one such type of imageprocessing technique. Examples of transform techniques are Hilbert transform, Fourier transform, Radon Transform, wavelet transform etc. Transform technique may be chosen based on its advantages, disadvantages and applications. The wavelet transform is a technique which assimilates the time and frequency domains and precisely popular as time-frequency representation of a non stationary signal. In this paper different types of Discrete wavelet transform is applied on an image. Comparative analysis of different wavelets such as Haar, Daubechies and symlet 2 is applied on image and different filters respond are plotted using MATLAB 15.
Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direct...
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ISBN:
(纸本)9781479981311
Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the "blind" environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR. In this research, a novel blind quality metric, measuring Geometric Distortions and image Complexity (GDIC), is proposed for DIBR-synthesized images. Firstly, a DIBR-synthesized image is decomposed into wavelet subbands by using discrete wavelet transform. Then, we adopt canny operator to capture the edge of wavelet subbands and compute the edge similarity between low -frequency subband and high frequency subbands. The edge similarity is used to quantify geometric distortions in DIBR-synthesized images. Secondly, a hybrid filter combining the autoregressive and bilateral filter is adopted to compute image complexity. Finally, the overall quality score is calculated by normalizing geometric distortions via image complexity. Experiments show that our proposed GDIC is superior to prevailing image quality assessment metrics, which were intended for natural and DIBR-synthesized images.
In digital imageprocessing, noise suppression from the original signal is still considered as biggest challenge till today. image denoising refers to the process in which it evaluates the unknown signal from the avai...
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ISBN:
(纸本)9789811307614;9789811307607
In digital imageprocessing, noise suppression from the original signal is still considered as biggest challenge till today. image denoising refers to the process in which it evaluates the unknown signal from the available noisy signal. Several algorithms are existing, which are proposed by other authors for denoising of an image like Discrete Cosine Transform (DCT), Discrete wavelet Transform (DWT), etc. This paper contributes itswork by discussing the significant work done in the area of image denoising along with advantages and disadvantages. After a brief discussion, classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed, which will help researchers in the image denoising area. The objective of this review paper is to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide an ease for selecting the ideal strategy according to the necessity.
imageprocessing techniques may be used for enhancing edges, boundaries, contrast, etc. of an image through accentuation or sharpening process. Many algorithms are available to normalize illumination impact for differ...
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ISBN:
(纸本)9781728118642
imageprocessing techniques may be used for enhancing edges, boundaries, contrast, etc. of an image through accentuation or sharpening process. Many algorithms are available to normalize illumination impact for different imageprocessingapplications. In this contribution, we conduct a comparative study on four different types of illumination normalization algorithms. They are based on discrete wavelet, logarithmic total variation with primal dual algorithm, histogram equalization technique, and morphological operation. In order to obtain better enhancement of image by using discrete wavelet based illumination normalization algorithm, selection of the particular wavelet is very important. Use of histogram equalization function in image preprocessing with other algorithms enhances the overall performance of face detection. This paper illustrates performance of different illumination normalization algorithms in Viola-Jones face detection system based on the extended Yale B database.
The brain-computer interface consists of connecting the brain with machines using the brainwaves as a mean of communication for several applications that help to improve human life. Unfortunately, Electroencephalograp...
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An imageprocessing is a coming out research which needs heed in bio-medical field. There are many imageprocessing methods which are useful to bring out information for analysis, memory space and conserves computing ...
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The new idea behind the CORDIC algorithm used for contemporary DSP applications is introduced in this work due to a very appealing and simple technique. This method not only reduces latency but also improves the throu...
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ISBN:
(纸本)9781665417594
The new idea behind the CORDIC algorithm used for contemporary DSP applications is introduced in this work due to a very appealing and simple technique. This method not only reduces latency but also improves the throughput of output parameters and also reduces the complexity of 2D –DCT computations. This paper describes an FPGA-based CORDIC processor that provides an efficient area with low latency and high frequency. The DCT processor has sought to eliminate multiplication operations by increasing the amount of shift and addition operations using the CORDIC method to reduce the complexity of computation. Application such as Speech waveform 1D- DCT is a highly helpful application in signalprocessing. This research provides the power performance study and FPGA implementation of the CORDIC algorithm work for 111.048 MHz frequency 2D-DCT using HDL simulation with the help of Xilinx ISE 14.7 and implemented it using Development board Altera Cyclone ii.
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