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A Framework For Single image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

A Framework For Single Image Dehazing Using DWT Based Cross ...

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International conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Sanjay K. Dwivedi Manoj Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (纸本)9781665474177

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.

关键词： Training Measurement Learning systems PSNR Benchmark testing Discrete wavelet transforms image restoration

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Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, Landsat 9 TIRS, and PlanetScope imagery with Machine Learning and Data Fusion

Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, L...

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International conference on Computer and applications (ICCA)

作者： Souleymane Bah M. D. Tanguy Mesreil Houegbe Emmanuel Wilfried G. Megnigbeto Vinasetan Ratheil Houndji Bignon Nicanor Kouton N. Gérard Gouwakinnou C. Eugène Ezin Institut de Mathématiques et de Sciences Physiques Université d'Abomey-Calavi Dangbo Bénin BiPLAC Laboratoire d'Ecologie de Botanique et de Biologie végétate Parakou Benin Division protection phytosanitaire Institut de Recherche sur le Coton Parakou Bénin Institut de Formation et de Recherche en Informatique Université d'Abomey-Calavi Abomey-Calavi Benin

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helping to reduce the use of pesticides. Given that traditional field inspection requires considerable effort with limited effectiveness, remote sensing combined with machine learning is an essential complement for large-scale, efficient monitoring. This study therefore aims to develop a machine learning-based solution for detecting cotton jassids from free satellite imagery data in Alibori, northern Benin. Multispectral and thermal infrared images respectively from the Sentinel-2 and Landsat 9 satellites were fused in the most correlated bands using signal processing algorithms Pseudo Wigner Distribution (PWD), Nonsubsampled Contourlet Transform (NSCT), and Discrete wavelet transform (DWT). Two configurations, in terms of data augmentation, were used. Areas infested by jassids were identified in merged and unmerged satellite images (Sentinel-2, Landsat 9, and PlanetScope, a commercial satellite) using Support Vector Machine (SVM), Random Forest (RF), and CatBoost supervised machine learning algorithms. The green and Thermal Infrared Sensor 1 (TIRS1) bands are the most correlated in configuration 1 and the blue and TIRSI bands in configuration 2 for respectively Sentinel-2 and Landsat 9. The DWT and NSCT signal processing algorithms produced the least Root Mean Square Deviation (RMSE) for image fusion (0.46 for DWT and 0.33 for NSCT). The best result in terms of detecting performance was obtained with the composite treatment of Landsat 9 images and the RF algorithm, with an overall accuracy of 88.23%, an Fl-score of 82%, and a Kappa index of 79.92% in configuration 1 and in configuration 2 for composite treatment of Sentinel-2 an overall accuracy of 92.36%, an Fl-score of 90.75 % and a Kappa index of 86.36% with CatBoost algorithm.

关键词： Radio frequency Support vector machines Satellites Machine learning algorithms signal processing algorithms Landsat Discrete wavelet transforms Satellite images Cotton Monitoring

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signal Restoration with Fractional Transform Sparse Representation and Autoencoder

Signal Restoration with Fractional Transform Sparse Represen...

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International conference on Computer and Communication Engineering, ICCCE

作者： Shivani Saxena Surbhi Mistry Nilesh Patel Mohendra Roy Ahsan Z. Rizvi Computer Sciences and Engineering Institute of Advanced Research Gandhinagar India Biotechnology and Bioengineering Institute of Advanced Research Gandhinagar India Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications.

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Novel multi-bit and bitwise minimum error replacement technique for watermarking and data hiding

Novel multi-bit and bitwise minimum error replacement techni...

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SICE Annual conference

作者： Rong-Jian Chen Su-Min Hung Yu-Cheng Peng Jui-Lin Lai Shi-Jinn Horng Department of Electronic Engineering National United University Miaoli Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781424476428

This paper presents an novel bitwise minimum error replacement (MER) technique for data hiding. Such a bitwise MER can embed k-bit (k ≥ 1) secret data into cover data only introduce minimum embedding error smaller than (2 k-1 - 1)2 i-k according to the embedding location i. To achieve the goal of MER, we develop novel multi-bit bitwise 3-step MER algorithms to adaptively evaluate the most similar value to replace the original one, which consist of three steps: (1) embed logo data into cover data, (2) adjust the least-significant bits (LSBs) of cover data, and (3) adjust the maximum-significant bits (MSBs) of cover data. Reason that we called these multi-bit MER algorithms are "bitwise" is that data replacements are based on the logic and algebraic bit operations to achieve minimum error. Many simulations show that the proposed MER performs good embedding quality for watermarking and steganography applications.

关键词： Watermarking PSNR Discrete wavelet transforms image color analysis Brightness Robustness signal processing algorithms

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Multimodal biometric person recognition system based on fingerprint & Finger-Knuckle-Print using correlation filter classifier

Multimodal biometric person recognition system based on fing...

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IEEE International conference on Communications (ICC)

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Sciences and Technology Houari Boumediene Algiers Algeria Department of Computer Science King Saud University Riyadh Saudi Arabia School of Computing Engineering and Information Sciences Northumbria University Newcastle-upon-Tyne UK

ISBN: (纸本)9781457720529

Biometrics is an effective technology for personnel identity recognition, but uni-modal biometric systems which use a single trait for recognition will suffer from problems like noisy sensor data, non-universality, lack of distinctiveness of the biometric trait, and spoof attacks. These problems can be tackled by using multi-biometrics in the system. Hand-based person recognition provides a reliable, low-cost and user-friendly viable solution for a range of access control applications. As one of the most popular biometric traits, fingerprints (FP) are widely used in personal recognition. However, a novel hand-based biometric feature, Finger-Knuckle-Print (FKP), has attracted an increasing amount of attention. In this paper, FP and FKP are integrated in order to construct an efficient multi-biometric recognition system based on matching score level and image level fusion. In this study we use the minimum average correlation energy (MACE) and Unconstrained MACE (UMACE) filters in conjunction with two correlation plane performance measures, max peak value and peak-to-sidelobe ratio, to determine the effectiveness of this method. The experimental results showed that the designed system achieves an excellent recognition rate on the Hong Kong polytechnic university (PolyU) FKP and high resolution fingerprint database.

关键词： Correlation Databases Training Discrete wavelet transforms Biomedical imaging Biometrics (access control) Fingers

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Multi-resolution Shape Analysis via Non-Euclidean wavelets: applications to Mesh Segmentation and Surface Alignment Problems

Multi-resolution Shape Analysis via Non-Euclidean Wavelets: ...

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IEEE conference on Computer Vision and Pattern Recognition

作者： Won Hwa Kim Moo K. Chung Vikas Singh Dept. of Computer Sciences University of Wisconsin Dept. of Biostatistics & Med. Informatics University of Wisconsin

ISBN: (纸本)9781467364102

The analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive Non-Euclidean wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks), We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.

关键词： Shape wavelet transforms Benchmark testing Surface waves Kernel wavelet analysis

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Saliency guided wavelet compression for low-bitrate image and Video coding

Saliency guided Wavelet compression for low-bitrate Image an...

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IEEE Global conference on signal and Information processing

作者： Souptik Barua Kaushik Mitra Ashok Veeraraghavan Rice University USA Indian Institute of Technology Madras India

ISBN: (纸本)9781479975921

We propose an improved saliency guided wavelet compression scheme for low-bitrate image/video coding applications. Important regions (faces in security camera feeds, vehicles in traffic surveillance) get degraded significantly at low bitrates by existing compression standards, such as JPEG/JPEG-2000/MPEG-4, since these do not explicitly utilize any knowledge of which regions are salient. We design a compression algorithm which, given an image/video and a saliency value for each pixel, computes a corresponding saliency value in the wavelet transform domain. Our algorithm ensures wavelet coefficients representing salient regions have a high saliency value. The coefficients are transmitted in decreasing order of their saliency. This allows important regions in the image/video to have high fidelity even at very low bitrates. Further, our compression scheme can handle several salient regions with different relative importance. We compare the performance of our method with the JPEG/JPEG-2000 image standards and the MPEG-4 video standard through two experiments: face detection and vehicle tracking. We show improved detection rates and quality of reconstructed images/videos using our Saliency Based Compression (SBC) algorithm.

关键词： Saliency wavelet transform image coding Video coding

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A Novel Method for Text and Non-Text Segmentation in Document images

A Novel Method for Text and Non-Text Segmentation in Documen...

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International conference on Communication and signal processing

作者： S. Deivalakshmi P. Palanisamy Gayatri Vishwanathan the Electronics and Communication Engineering Department National Institute of Technology Tiruchirappalli TN 620015 India

ISBN: (纸本)9781467348652

Segmentation of the contents of document images into text and non-text regions is an essential pre-processing step for applications such as document analysis and classification, as well as OCR. This paper presents a novel technique to segment the document image into text and non-text regions using a combination of wavelet-based Gray Level Co-Occurrence Matrix (GLCM) features and K-means clustering. A comparison between the performances of different wavelets in document image segmentation is also performed and tabulated. The technique was tested on a number of scanned article images from the MediaTeam Document Database and results show a marked improvement over the already existing method based on GLCM features.

关键词： Discrete wavelet Transform Document image Segmentation GLCM Features K-means Clustering Algorithm

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Region Based Lossless Compression for Digital images in Telemedicine Application

Region Based Lossless Compression for Digital Images in Tele...

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International conference on Communication and signal processing

作者： B. Brindha G. Raghuraman the Computer Science and Engineering Department SSN College of Engineering Chennai.

ISBN: (纸本)9781467348652

Region Based Coding Technique is significant for medical image compression and transmission. In medical images, only a small portion of the image might be diagnostically useful. In these regions, lossless compression can helps to achieve high efficiency performance in telemedicine applications. This paper proposes a very efficient and low complexity compression method for Digital Imaging and Communications in Medicine (DICOM) images. Main advantages of Region based coding technique is exploited in this paper. Segmentation of these regions leads to optimal performance. Here the ROI part of the image is identified by manually and combined with effect of Integer wavelet Transform (IWT). IWT compression technique is useful to reconstruct the original image, reversibly with desired quality. The overall compression process helps to reach a satisfactory level for image transmission in limited bandwidth over a telemedicine application.

关键词： Compression Discrete Cosine Transform (DCT) Integer wavelet Region of Interest (ROI)

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Scintigraphic images restoration using jointly Fourier and wavelet domains

Scintigraphic images restoration using jointly Fourier and W...

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3rd International conference on Information and Communication Technologies, vol.2

作者： Nejla Gribaa Nawres Khlifa Kamel Hamrouni Search unit of Signal processing Image processing and Pattern Recognition National Engineering School of Tunis (ENIT) Tunisia

The paper focus on the problem of scintigraphic images restoration. These images are often disturbed by the bad detection equipment quality. The aim is to improve regions of interest perception, to help useful information extraction and so to allow good understanding of the pathological phenomenon. The restoration is an ill-posed problem. So, inverting the distortion model in presence of additive noise is often numerically unstable. We propose, then, a new framework based on the Fourier and the wavelet domain, in order to benefit from the advantages of each one. The Fourier regularized deconvolution exploits the Fourier representation efficiency of the colored noise. Whereas the wavelet packets denoising exploits the wavelet representation efficiency and the good localization of inherent noise in this domain. We noticed the performance of the proposed method in terms of edges preservation, contrast and uniformity in the images.

关键词： Nuclear medicine image restoration Regularisation wavelet packets transform Fourier transform

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