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arXiv 2025年

作者： Wu, Haofan Huang, Yin Wu, Yuqing Yang, Qiuyu Wang, Bingfang Zhang, Li Khan, Muhammad Fahadullah Zia, Ali Memon, M. Saleh Bukhari, Syed Sohail Memon, Abdul Fattah Ji, Daizong Zhang, Ya Mustafa, Ghulam Fang, Yin Research Center for Translational Medicine Medical Innovation Center State Key Laboratory of Cardiology Shanghai East Hospital Shanghai200120 China The Institute for Biomedical Engineering & Nano Science Tongji University School of Medicine Shanghai200120 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Ophthalmology Tongji Hospital School of Medicine Tongji University Shanghai200000 China Isra Postgraduate Institute of Ophthalmology Karachi Pakistan The Eye Centre South City Hospital Karachi Pakistan Center for the Development of Laboratory Equipment Pakistan Council of Scientific and Industrial Research Karachi Pakistan

High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variability, and patient compliance, fundus images often suffer from low resolution and signal-to-noise ratio. Recent years have witnessed promising progress in fundus image enhancement. However, existing works usually focus on restoring structural details or global characteristics of fundus images, lacking a unified image enhancement framework to recover comprehensive multi-scale information. Moreover, few methods pinpoint the target of image enhancement, e.g., lesions, which is crucial for medical image-based diagnosis. To address these challenges, we propose a multi-scale target-aware representation learning framework (MTRL-FIE) for efficient fundus image enhancement. Specifically, we propose a multi-scale feature encoder (MFE) that employs wavelet decomposition to embed both low-frequency structural information and high-frequency details. Next, we design a structure-preserving hierarchical decoder (SHD) to fuse multi-scale feature embeddings for real fundus image restoration. SHD integrates hierarchical fusion and group attention mechanisms to achieve adaptive feature fusion while retaining local structural smoothness. Meanwhile, a target-aware feature aggregation (TFA) module is used to enhance pathological regions and reduce artifacts. Experimental results on multiple fundus image datasets demonstrate the effectiveness and generalizability of MTRL-FIE for fundus image enhancement. Compared to state-of-the-art methods, MTRL-FIE achieves superior enhancement performance with a more lightweight architecture. Furthermore, our approach generalizes to other ophthalmic image processing tasks without supervised fine-tuning, highlighting its potential for clinical applications. The dataset, code, and models will be made publicly available at https://***/RyanWu31/MTRL-FIE. Copyright © 2025, The

关键词： image enhancement

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Generalized Gaussian scale mixtures: A model for wavelet coefficients of natural images

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signal processing-image COMMUNICATION 2018年 66卷 87-94页

作者： Gupta, Praful Moorthy, Anush Krishna Soundararajan, Rajiv Bovik, Alan Conrad Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Netflix Inc Los Angeles CA USA Indian Inst Sci Dept Elect Commun Engn Bangalore Karnataka India

We develop a Generalized Gaussian scale mixture (GGSM) model of the wavelet coefficients of natural and distorted images. The GGSM model, which is more general than and which subsumes the Gaussian scale mixture (GSM) model, is shown to be a better representation of the statistics of the wavelet coefficients of both natural as well as distorted images. We demonstrate the utility of the model by applying it to various image processing applications, including blind distortion identification and no reference image quality assessment (NR-IQA). Similar to the GSM model, the GGSM model is useful for motivating the use of local divisive energy normalization, especially when the wavelet coefficients are computed on distorted pictures. We show that the GGSM model can lead to improved performance in distortion-related applications, while providing a more principled approach to the statistical processing of distorted image signals. The software release of a GGSM-based NR-IQA approach called DIIVINE-GGSM is available online at http://***/research/quality/*** for further experimentation.

关键词： Generalized Gaussian scale mixture model Distorted image modeling Distortion-identification No-reference image quality assessment

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ASSESSING SELF-SIMILARITY IN REDUNDANT COMPLEX AND QUATERNION wavelet DOMAINS: THEORY AND applications

ASSESSING SELF-SIMILARITY IN REDUNDANT COMPLEX AND QUATERNIO...

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作者： Taewoon Kong Georgia Institute of Technology

学位级别：博士

Theoretical self-similar processes have been an essential tool for modeling a wide range of real-world signals or images that describe phenomena in engineering, physics, medicine, biology, economics, geology, chemistry, and so on. However, it is often dif- ficult for general modeling methods to quantify a self-similarity due to irregularities in the signals or images. wavelet-based spectral tools have become standard so- lutions for such problems in signal and image processing and achieved outstanding performances in real applications. This thesis proposes three novel wavelet-based spectral tools to improve the as- sessment of self-similarity. In Chapter 2, we propose spectral tools based on non-decimated complex wavelet transforms implemented by their matrix formulation. This non-decimated complex wavelet spectra utilizes both real and imaginary parts of complex-valued wavelet co- efficients via their modulus and phase. A structural redundancy in non-decimated wavelets and a componential redundancy in complex wavelets act in a synergy when extracting wavelet-based informative descriptors. In particular, we suggest an im- proved way of separating signals and images based on their scaling indices in terms of spectral slopes and information contained in the phase in order to improve per- formance of classification. It is also worth mentioning that the proposed method can handle signals of an arbitrary size and in 2-D case, rectangular images of pos- sibly different and non-dyadic dimensions because of the matrix formulation of non- decimated wavelet transform. This is in contrast to the standard wavelet transforms where algorithms for handling objects of non-dyadic dimensions requires either data preprocessing or customized algorithm adjustments. Quaternion wavelets are another redundant wavelet transforms generalizing complex- valued wavelet transforms. In Chapter 3, we step into the quaternion domain and propose a matrix-formulation for non-decimated quaternion wave

关键词： wavelets Classification Feature extraction

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Performances Improvement of Chaos-Based Robust Digital image Watermarking using Advanced Techniques 6

Performances Improvement of Chaos-Based Robust Digital Image...

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6th International Conference on image and signal processing and their applications, ISPA 2019

作者： Kaibou, Redouane Azzaz, Mohamed Salah École Militaire Polytechnique Laboratoire Systèmes Électroniques et Numériques Alger16111 Algeria

ISBN: (纸本)9781728131566

This paper presents a research study leading to a robust chaos-based scheme for digital image watermarking applications in the transform domain. The steps undergone include combining, in a repeating process, three performant conventional mathematical transforms of Direct Cosine Transform (DCT), Discrete wavelet Transform (DWT) and variants with three different state-of-the-art advanced techniques namely Discrete Curvelet Transform (DCuT), Discrete Radon Transform (DRaT) and Finite Ridgelet Transform (FRT). Results have shown a neat improvements in watermarking imperceptibility and robustness each time especially after including Singular Value Decomposition (SVD) which has enhanced embedding capability as well. Methods evaluation has followed an exhaustive series of performed tests that applied a range of evaluation metrics for imperceptibility. In addition, Robustness of the final scheme has also been confirmed by applying a variety of attacks on the resulting images. Finally, security aspect has also been improved after a comparative implementations of popular four chaotic models namely Arnold Cat Map (ACM), Lorenz Cat Map (LCM), Rossler Cat Map (RCM) and Genesio-Tesi Cat Map (GTCM) respectively producing secure images encryption levels. All Implementations have been validated using a sufficent number of color image from a large dataset. © 2019 IEEE.

关键词： Cosine transforms

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Hand Movement Recognition Using Cross Spectrum image Analysis of EMG signals-A Deep Learning Approach

Hand Movement Recognition Using Cross Spectrum Image Analysi...

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Sustainable Technology and Engineering applications (NCETSTEA), 2020 National Conference on Emerging Trends on

作者： Sayanjit Singha Roy Kaniska Samanta Soumya Chatterjee Sayantan Dey Arnab Nandi Ronjoy Bhowmik Shoumosree Mondal Electrical Engineering Department Techno India University Kolkata India Electrical Engineering Department St. Thomas’ College of Engineering and Technology Kolkata India

ISBN: (数字)9781728143620

ISBN: (纸本)9781728143637

Accurate recognition of hand movements using electromyography (EMG) signals is important to develop robust human-machine interface system for upper limb prosthetic control. For accurate detection and classification of different movements, proper feature selection from EMG signals is necessary, failure of which may often lead to incorrect results. To this end, this article presents an image processing aided deep learning approach for detection and classification of hand movement EMG signals. In this study, EMG signals for both left hand and right hand movements were procured from an existing database. A reference signal for each category was chosen as reference and cross spectrum of the rest of the EMG signals was done with the reference signal. The resultant cross-wavelet spectrum images of both classes of EMG signals were fed to a pre-trained convolution neural network (CNN) for the purpose of detection and classification of hand movements. It has been observed that the proposed method returned an average classification accuracy of 97.6% in segregating different categories of EMG signals. Besides, the performance of the proposed method analyzed using different CNN architectures was also found to be robust. The proposed method can be implemented for real-time detection of hand movements.

关键词： Convolution neural network classification cross-wavelet transform electromyography and hand movement hand movement Taxonomy Electromyogram cross spectrum wavelet transforms signals Reference signals Movements

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A Review on Digital image Watermarking Using Transformation and Optimization Techniques

A Review on Digital Image Watermarking Using Transformation ...

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International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

作者： Lalita Verma Sanjay Pratap Singh Chauhan School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9781728183374

ISBN: (纸本)9781728183381

Now-a-days multimedia data is being generated very frequently in text format, image format, audio format etc. Security is the major issue of the multimedia data. Multimedia data can be very easily modified by the intruders. By using Watermarking and Steganography techniques the multimedia data can be protected from unauthorised access. Watermarking technique hides the data in the carrier signal. In the process of Steganography, the digital data (e.g. digital files like audio, video, text etc) can be embedded into some other digital file. The concept of watermarking is to keep the watermark robust from various attacks. Whenever the intruder tries to temper with the digital signal and removes the watermark, the digital data becomes useless as the quality is degraded. This paper discusses about various applications of watermarking, characteristics of Watermarking and basics of image processing. This paper also provides a comprehensive study of available literature on digital image watermarking. Some of the optimization techniques have also been discussed.

关键词： wavelet transforms Digital images image processing Watermarking Streaming media Optimization

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An Efficient High-Speed Lifting Based 1D/2D-DWT VLSI Architecture Using CDF-5/3 wavelet Transform For image processing applications

An Efficient High-Speed Lifting Based 1D/2D-DWT VLSI Archite...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： M Sushmitha S Chetan Sayantam Sarkar VLSI Design and Embedded Systems Dr. Ambedkar Institue of Technology Bengaluru India Dr. Ambedkar Institue of Technology Bengaluru India Vijaya Vittala Institue of Technology Bengaluru India

ISBN: (数字)9781728197722

ISBN: (纸本)9781728197739

There are various Discrete wavelet Transform architectures that are designed to fulfil certain requirements and criteria's. The convolution method which is an old traditional method which requires more multipliers, hardware resources and huge memory storage which is not apt to yield high speed and efficient image processing, signal processing application designs when compared to lifting method. In this paper, we have proposed an architecture for lifting scheme based CDF-5/3 2D-DWT, which includes less mathematical computations and high speed is achieved compared to existing works. The proposed architecture design is multiplier-less and utilizes lesser number of components hence it is area efficient and also achieves high throughput rate. The RTL of this design is described in VHDL language and is synthesized in Xilinx 14.5 and target device is Virtex-5 series XC5VLX110T for testing the resulting parameters like LUTs, flip-flops and slices. Higher PSNR values and lesser MSE values are achieved when tested for different kind of images. The maximum operating frequency achieved for the designed 2D-DWT architecture is 258.358MHz. Hence the proposed 2D-DWT architecture has increased speed, less computational complexity and good hardware utilization when compared to other existing works.

关键词： Discrete wavelet transforms Finite impulse response filters image coding Two dimensional displays Very large scale integration Convolution Clocks

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Orthogonal Ramanujan Sums, Its Properties, and applications in Multiresolution Analysis

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IEEE TRANSACTIONS ON signal processing 2018年第21期66卷 5789-5798页

作者： Yadav, Devendra Kumar Kuldeep, Gajraj Joshi, Shiv Dutt SAG Delhi 110054 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

signal processing community has recently shown interest in Ramanujan sums, which were defined by S. Ramanujan in 1918. In this paper, we propose a family of sequences, termed here as orthogonal Ramanujan sums, C-q(k)(n) and also derive their various properties. We also prove two new properties of the Ramanujan Sums, namely, shifted multiplication property and interpolation property. These properties are used to define higher order orthogonal Ramanujan sums. These sums are periodic in nature and are orthogonal for different values of k and q. Using orthogonal Ramanujan sums, a new representation of finite length signal is given, termed here as orthogonal Ramanujan periodic transform. The coefficients obtained using this transform can be divided into two parts, first coefficient gives smoothing component and the rest give the detail components of the signal. Further, application of the orthogonal Ramanujan sums in discrete wavelet transform is developed. Construction of Ramanujan wavelet analysis matrix for applying discrete wavelet transform on a signal is described. The application of this matrix on an image of size N x N, where N is divisible by integer q will result in q(2) - 1 detail components and one average component in transform domain. Some of the applications of this transform are also demonstrated, especially in the field of image processing.

关键词： Ramanujan sums orthogonal Ramanujan sums orthogonal Ramanujan periodic transform multi resolution analysis discrete wavelet transform

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Projected Improved Fista And Application To image Deblurring

Projected Improved Fista And Application To Image Deblurring

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IEEE International Conference on image processing

作者： Praveen Kumar Pokala Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

The analysis-sparse model has been shown to be efficient as compared to the synthesis-sparse model when the sparsifying transform is redundant particularly for image restoration applications. We pose the image deblurring problem as an optimization problem based on the analysis-sparse model considering the sparsifying basis to be a tight frame, more specifically, the shift-invariant discrete wavelet transform (SIDWT). We propose two algorithms, namely, projected improved fast iterative soft-thresholding algorithm (piFISTA) and projected improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (piFISTA-BN). The proposed algorithms are the analysis counterparts of the improved fast iterative soft-thresholding algorithm (iFISTA) and improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (iFISTA-BN), respectively, both of which consider the synthesis-sparse model. We demonstrate that piFISTA and piFISTA-BN significantly outperform FISTA, pFISTA, iFISTA, and iFISTA-BN considering standard objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Further, we demonstrate empirically that the proposed algorithms converge faster than the state-of-the-art techniques.

关键词： Analytical models image restoration Transforms image reconstruction Iterative algorithms Convergence Standards

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image Segmentation Based Hybrid Watermarking Algorithm for Copyright Protection

Image Segmentation Based Hybrid Watermarking Algorithm for C...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Y. Bhavani Sai Srikar Puppala Spoorthy Shivani Pabba Kavya Sri Kasarla Kadipaka Anvitha Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Today, most of the information shared across the globe is in multimedia form like audio, video and image. With increased globalization, any type of such information can be broadcasted throughout the world easily and is accessible to multiple users for varied applications. This may lead to fraudulent claiming of data ownership which makes copyright protection a major priority in today's world. Digital Watermarking serves as a prime solution to copyright protection where we embed watermark into digital image which serves as an authenticator. In this paper, we proposed a hybrid algorithm using image segmentation, SVD (Singular value decomposition), DWT (Discrete wavelet transformation), Homomorphic filtering and RSA encryption technique that helps to overcome various threats. Here, we proposed a new approach of digital watermarking by embedding two segmented watermarks which are obtained using two distinct threshold values on the original watermark. These threshold values are sent as secret keys to the receiver using Diffie-Hellman key exchange algorithm and can be used at the time of copyright verification. This approach of hybrid technique is being tested by applying Gaussian Noise and it gave the best results when compared with the other digital watermarking techniques.

关键词： Watermarking Discrete wavelet transforms image segmentation signal processing algorithms Discrete cosine transforms Hafnium Copyright protection

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