- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis
摘要： Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.
关键词： sub-band,discrete wavelet transform (DWT),extreme learning machine (ELM),kurtosis,Blind noisy image quality assessment
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Chennai (2018.3.22-2018.3.24)] 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Automatic Segmentation of Exudates in Retinal Images
摘要： This paper presents a new technique for segmentation of exudates in fundus images. This technique is based on Discrete Wavelet Transform (DWT) and histogram based thresholding procedure. In this work, Optic Disc (OD) is eliminated using DWT from original green component image prior segmentation of exudates. This step aids to avoid the misclassification of exudates region. Histogram based threshold calculation procedure is introduced for segmentation of bright regions in green component image. Hard exudates are obtained after masking the OD region in segmented bright regions of the green component image. This technique was evaluated on images from DIARETDB0 and DIARETDB1 databases. The average sensitivity, specificity and accuracy achieved by proposed method are 0.7890, 0.9972 and 0.9964 respectively. Comparison with existing methods offered in the literature shows that the performance of proposed approach is significant.
关键词： Optic Disc,Retinal image,Segmentation,Exudates,Discrete Wavelet Transform
更新于2025-09-23 15:22:29
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Infrared super-resolution imaging using multi-scale saliency and deep wavelet residuals
摘要： Infrared (IR) imaging systems with low-density focal plane arrays produce images with poor spatial resolution. To address this limitation, super-resolution (SR) algorithms can be applied on IR-low resolution (LR) images. In this paper, we present a new SR technique based on the multi-scale saliency detection and the residuals learned by the deep convolutional neural network (CNN) in the wavelet domain (DWCNN). The input LR image is processed in the transformed domain by applying 2D discrete wavelet transform. It decomposes an image into its low-frequency and high-frequency subbands. The multi-scale saliency detection is used to extract small scale and large scale salient feature maps from the bicubic upscaled LR image. These maps are incorporated in the high-frequency subbands of the LR image. Furthermore, the low-frequency and high-frequency subands are re?ned using the residuals learned by the DWCNN in training phase. The proposed algorithm is compared with the conventional and state-of-the-art SR methods. Results indicate that our method yields good reconstruction quality with high peak signal to ratio, structural similarity and low blur indices. Besides, our method requires less computational time.
关键词： Infrared imaging,Convolutional neural network,Discrete wavelet transform,Multi-scale saliency,Super-resolution
更新于2025-09-23 15:22:29
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Evaluating Feature Extractors and Dimension Reduction Methods for Near Infrared Face Recognition Systems
摘要： This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPDA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments.
关键词： comparative study,undecimated discrete wavelet transform,Face recognition,near infrared,Zernike moments
更新于2025-09-23 15:22:29
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GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy C-Mean Clustering
摘要： Change detection in remote sensing images turns out to play a significant role for the preceding years. Change detection in synthetic aperture radar (SAR) images comprises certain complications owing to the reality that it endures from the existence of the speckle noise. Hence, to overcome this limitation, this paper intends to develop an improved model for detecting the changes in SAR image. In this model, two SAR images captivated at varied times will be considered as the input for the change detection process. Initially, discrete wavelet transform (DWT) is employed for image fusion, where the coefficients are optimized using improved grey wolf optimization (GWO) called adaptive GWO (AGWO) algorithm. Finally, the fused images after inverse transform are clustered using fuzzy C-means (FCM) clustering technique and a similarity measure is performed among the segmented image and ground truth image. With the use of all these technologies, the proposed model is termed as adaptive grey wolf-based DWT with FCM (AGWDWT-FCM). The similarity measures analyze the relevant performance measures such as accuracy, specificity and F1 score. Moreover, the performance of the AGWDWT-FCM in change detection model is compared to other conventional models, and the improvement is noted.
关键词： Filter coefficient,Adaptive discrete wavelet transform,Grey wolf optimization,Synthetic aperture radar,Fuzzy C-means clustering
更新于2025-09-23 15:21:21
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No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features
摘要： No-reference image quality assessment (NR-IQA) aims to evaluate the perceived quality of distorted images without prior knowledge of pristine version of the images. The quality score is predicted based on the features extracted from the distorted image, which needs to correlate with the mean opinion score. The prediction of an image quality score becomes a trivial task, if the noise affecting the quality of an image can be modeled. In this paper, gradient magnitude and Wiener filtered discrete wavelet coefficients are utilized for image quality assessment. In order to reconstruct an estimated noise image, Wiener filter is applied to discrete wavelet coefficients. The estimated noise image and the gradient magnitude are modeled as conditional Gaussian random variables. Joint adaptive normalization is applied to the conditional random distribution of the estimated noise image and the gradient magnitude to form a feature vector. The feature vector is used as an input to a pre-trained support vector regression model to predict the image quality score. The proposed NR-IQA is tested on five commonly used image quality assessment databases and shows better performance as compared to the existing NR-IQA techniques. The experimental results show that the proposed technique is robust and has good generalization ability. Moreover, it also shows good performance when training is performed on images from one database and testing is performed on images from another database.
关键词： Wiener filtering,Gradient magnitude,Discrete wavelet transform,No-reference image quality assessment
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) - Tamilnadu, India (2019.4.11-2019.4.13)] 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) - An Efficient MRI-PET Medical Image Fusion using Non-Subsampled Shearlet Transform
摘要： Multimodal medical image fusion is an effective method to incorporate the pertinent details from a variety of medical images into a solitary image. The outcome would be more factual than any of the input source images. Here, an efficient integrative scheme based on Non- Subsampled Shearlet transform in YIQ color space is considered. The suggested approach is well in enhancing boundary points in medical image analysis, data conveying points utilized to reveal the stronger visual framework of the image. The similar experimental outcomes and investigation make visible that the proposed strategy gives more enhanced results with reference to some assessment measures. Our proposed method can improve the information content, visual quality and the edge information simultaneously.
关键词： Average Combination Rule,Curvelet Transform,Discrete Wavelet Transform,Non-Subsampled Shearlet Transform,Choose Max Fusion Rule,Undecimated Wavelet Transform
更新于2025-09-16 10:30:52
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Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis
摘要： In research and development laboratories, chemical or pharmaceutical analysis has been carried out by evaluating sample signals obtained from instruments. However, the qualitative and quantitative determination based on raw signals may not be always possible due to sample complexity. In such cases, there is a need for powerful signal processing methodologies that can effectively process raw signals to get correct results. Wavelet transform is one of the most indispensable and popular signal processing methods currently used for noise removal, background correction, differentiation, data smoothing and filtering, data compression and separation of overlapping signals etc. This review article describes the theoretical aspects of wavelet transform (i.e., discrete, continuous and fractional) and its characteristic applications in UV spectroscopic analysis of pharmaceuticals.
关键词： UV spectroscopy,continuous wavelet transform,discrete wavelet transform,pharmaceutical analysis,fractional wavelet transform
更新于2025-09-10 09:29:36
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Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images
摘要： Glaucoma is a class of eye disorder; it causes progressive deterioration of optic nerve fibres. Discrete wavelet transforms (DWTs) and empirical wavelet transforms (EWTs) are widely used methods in the literature for feature extraction using image decomposition. However, to increase the accuracy for measuring features of images a hybrid and concatenation approach has been presented in the proposed research work. DWT decomposes images into approximate and detail coefficients and EWT decomposes images into its sub band images. The concatenation approach employs the combination of all features obtained using DWT and EWT and their combination. Extracted features from each of DWT, EWT, DWTEWT and EWTDWT are concatenated. Concatenated features are normalised, ranked and fed to singular value decomposition to find robust features. Fourteen robust features are used by support vector machine classifier. The obtained accuracy, sensitivity and specificity are 83.57, 86.40 and 80.80%, respectively, for tenfold cross validation which outperforms the existing methods of glaucoma detection.
关键词： glaucoma,empirical wavelet transform,support vector machine,discrete wavelet transform,feature extraction
更新于2025-09-10 09:29:36
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[IEEE 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Athens, Greece (2018.7.4-2018.7.6)] 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - A Novel Discrete Wavelet Transform based Coherent Optical OFDM System
摘要： In optical communication, which is an important part of the Radio over Fiber (RoF) systems, coherent optical OFDM (CO-OFDM) systems allow the use of amplitude and phase belong to the light at the same time for data transmission. However, the data transmission speed and distance of the CO-OFDM system are limited by optical channel impairment effects. One of the major disadvantages of CO-OFDM systems is its sensitivity to fiber nonlinearity effects. For this reason, in order to be used the new signal processing techniques efficiently in the receiver, the optical channel information must be estimated and the received signal must be equalized. In this work, different equalizer constructions are investigated and a frequency domain channel equalizer used in the novel DWT-based CO-OFDM system has been proposed. With the simulation studies made, the equalizers were compared and the results were given with different changes. From the obtained simulation results, it is seen that the proposed method provides about 5.5 OSNR gain for 1E-4 BER value.
关键词： fiber nonlinearity impairment,discrete wavelet transform,Radio over Fiber,CO-OFDM systems,frequency domain equalizer
更新于2025-09-10 09:29:36