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Structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images 6th

Structure-Texture Decomposition-Based Enhancement Framework ...

引用

6th International conference on Computer Vision and image processing, CVIP 2021

作者： Haritha, K.M. Sreeni, K.G. Zacharias, Joseph Jeena, R.S. Department of Electronics and Communication Engineering College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (纸本)9783031113451

images acquired in poor illumination conditions are characterized by low brightness and considerable noise which constrain the performance of computer vision systems. image enhancement thus remains crucial for improving the efficiency of such systems. To improve the visibility of low-light images, a novel image enhancement framework based on the structure-texture decomposition is proposed in this paper. Firstly, the low-light image is split into structure and texture layers using the total-variation (TV) based image decomposition approach. The structure layer is initially diffused using Perona-Malik (PM) diffusion model and the local and global luminance enhancement is incorporated in the structure-pathway using an expanded model of biological normalization for visual adaptation. In the texture pathway, the suppression of local noise and the enhancement of image details are attained with the estimation of the local energy of the texture layer and the strategy of energy weighting. Eventually, the final enhanced image of improved quality is obtained by merging the modified structure and texture layers. The effectiveness of the proposed framework is validated using no-reference image quality metrics including IL-NIQE, BRISQUE, PIQE, and BLIINDS-II. The experimental results show that the proposed method outperforms the state-of-the-art approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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Long Range Imaging Using Multispectral Fusion of RGB and NIR images

Long Range Imaging Using Multispectral Fusion of RGB and NIR...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hao Zhang Lin Mei Cheolkon Jung School of Electronic Engineering Xidian University Xian China

Unlike RGB images, NIR images are robust to atmospheric environments such as Rayleigh scattering. In this paper, we propose long-range imaging using multispectral fusion of RGB and NIR images. The proposed network consists of feature extraction, feature fusion, and reconstruction. For feature extraction, we adopt pyramid feature selection to capture multi-scale information. For feature fusion, we present a learnable attention based fusion block (AFB) to refine and fuse latent features of RGB and NIR images and a multi-scale fusion block (MFB) to integrate the fused feature in multiple scales. For reconstruction, we use three convolutions corresponding to the encoder to reconstruct the fusion image. Besides, we perform image registration based on scale-invariant feature transform (SIFT) to align NIR image referring to RGB image. Experimental results show that the proposed network successfully recovers hidden textures lost in RGB images while keeping colors and outperforms state-of-the-art fusion methods in terms of both visual quality and quantitative measurements.

关键词： image registration visualization Rayleigh scattering image color analysis Fuses Imaging Transforms

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SSFMamba: Spatial-Spectral Fusion State Space Model for Pansharpening

SSFMamba: Spatial-Spectral Fusion State Space Model for Pans...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mengting Ma Mengjiao Zhao Yizhen Jiang Xiangdong Li Wei Zhang College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China Innovation Center of Yangtze River Delta Zhejiang University Jiaxing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pansharpening aims to fuse the panchromatic (PAN) and low-resolution multispectral (LR-MS) images, finally generating the high-resolution multispectral (HR-MS) images by reconstructing the spatial-spectral properties. Recently, VMamba-based methods built upon the visual state space (VSS) have shown great potential in pansharpening, but they are unable to explicitly characterize the spatial-spectral properties from the given LR-MS and PAN images. Thus, we propose spatial-spectral fusion state space model (SSFMamba), which consists of multi-scale spatial-wise visual state space (MSpa-VSS) block, bi-directional spectral-wise visual state space (BSpe-VSS) block, and gated spatial-spectral fusion (GSSF) block. Specifically, the MSpa-VSS block introduce multi-scale convolution opeartion in the VSS, thus simultaneously modelling spatial dependencies and capturing multi-scale spatial property; the BSpe-VSS block bi-directionally scans the spectral channels for learning continuous spectral property; moreover, we design the GSSF block for fusing spatial-spectral properties in the adaptive manner. Extensive experimental results on several datasets demonstrate the superiority performance of the proposed SSFMamba.

关键词： visualization Adaptation models Fuses Convolution Pansharpening Bidirectional control Logic gates Acoustics Speech processing image reconstruction

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A Deep Learning Approach to Face Detection and Recognition in Group Photos Using OpenCV

A Deep Learning Approach to Face Detection and Recognition i...

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International conference on Advances in Electronics, Computers and communications (ICAECC)

作者： Mutyala Sai Sri Siddhartha Bobburi Sai Kowshik Mora Venkata Dhanumjaya Reddy Suja Palaniswamy Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

Face detection and face recognition are the most important and frequently used operations in image processing and computer vision domains. There are many applications for face recognition, but some of them require accurate performance in real-time with pose, illumination, and occlusion invariant. In this work, a method has been proposed to detect and recognize a person in various backgrounds, clothes and among the crowd. VGGFace and MTCNN are used in this work for the implementation where it compares a person's face with other images for similar features and provides a similarity score. The proposed method resulted in 97% accuracy. A custom-developed Amrita Students image Database (ASID) has been used in this work.

关键词： Deep learning Accuracy image databases Face recognition image processing Lighting Real-time systems

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Geological image Defect processing Based on ResNet50 with Transfer Learning in visual Communication

Geological Image Defect Processing Based on ResNet50 with Tr...

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Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： Xiaona Wang Shandong Business Institute Yantai

In geological research, classification of rock image is a significant role. However, existing deep learning (DL) techniques rock classification accuracies are unsatisfied because of network model’s weak feature extraction. This paper implements a ResNet50 model that uses the transfer learning (TL) approach to develop an automatic classification model for rock image classification. ResNet50 creates the residual structure to enhance its performance in image classification by extracting high-quality features from rock images. When there are fewer rock images, the TL approach better express the rock texture information for classification by abstracting the deep features from shallow features. Rock and imageNet datasets are used to analyze this model. The implemented method outperformed better performance than existing methods such as ResNet34, Triplet-EfficientNet, and InceptionResnetV2, in terms of 95.90% accuracy using rock dataset, Compared to You Only Look Once (YOLO) version 4, implemented ResNet50 achieved 99.99% accuracy, 99.99% precision, 99.90% recall, and 99.95% f-measure using imageNet dataset.

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Self-Supervised Incremental Learning of Object Representations from Arbitrary image Sets

Self-Supervised Incremental Learning of Object Representatio...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： George Leotescu Alin-Ionut Popa Diana Grigore Daniel Voinea Pietro Perona Amazon Inc. California Institute of Technology

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Computing a comprehensive and robust visual representation of an arbitrary object or category of objects is a complex problem. The difficulty increases when one starts from a set of uncalibrated images obtained from different sources. We propose a self-supervised approach, Multi-image Latent Embedding (MILE), which computes a single representation from such an image set. MILE operates incrementally, considering one image at a time, while processing various depictions of the class through a shared gated cross-attention mechanism. The representations are progressively refined as more available images are incorporated, without requiring additional training. Our experiments on Amazon Berkeley Objects (ABO) and iNaturalist demonstrate the effectiveness in two tasks: object or category-specific image retrieval and unsupervised context-conditioned object segmentation. Moreover, the proposed multi-image input setup opens new frontiers for the task of object retrieval. Our studies indicate that our models can capture descriptive representations that better encapsulate the intrinsic characteristics of the objects. Our code is available at https://***/amazon-science/mile.

关键词： Training visualization Computer vision Incremental learning Codes Noise image retrieval Object segmentation Logic gates Resilience

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Training-Free Open-Vocabulary Segmentation of Aerial Drone images Using Foundation Models

Training-Free Open-Vocabulary Segmentation of Aerial Drone I...

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ieee International conference on Intelligent Computer Communication and processing (ICCP)

作者： Oana Morar Andra Petrovai Technical University of Cluj-Napoca Romania

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

Semantic and instance segmentation have seen sig-nificant advancements, yet their application to aerial imagery faces numerous challenges. The high complexity of aerial images which often capture extensive scenes with numerous objects and varying object scales, differ considerably from the simpler natural images that the visual deep learning models are usually trained on. Additionally, the scarcity of annotated datasets for pixel-level semantic and instance segmentation in aerial imagery, hinders the development of specialized systems. However, re-cent progress in foundational models trained on vast, diverse datasets has improved generalization capabilities, making them robust across various scenarios. These models can be applied directly or adapted with minimal annotated data, significantly reducing the need for extensive labeled datasets. Even though foundation models are consecrated for their high generality, the results on specific scenarios, such as aerial images, are not as good as expected. In this paper, we provide an automatic panoptic segmentation and annotation system adapted to the characteristics of the drone vision images based on the visual foundation models Grounding DINO, SAM (Segment Anything Model) and the visual language model CLIP. We propose an algorithm that combines the foundation models and includes processing techniques to improve the performance on aerial scenes, eliminating the need for pixel-level annotated training data. The algorithm can segment objects using a fixed number of semantic classes or segment new ones through a text input by the user. We also provide a demo web application to test our solution on images from the UAVid dataset.

关键词： Instance segmentation Deep learning image segmentation visualization Adaptation models Annotations Computational modeling Semantics Training data Drones

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Domain Randomization on Deep Learning Models for image Dehazing 27

Domain Randomization on Deep Learning Models for Image Dehaz...

引用

27th National conference on communications (NCC)

作者： Shamsuddin, Abdul Fathaah Abhijith, P. Ragunathan, Krupasankari Deepak, Raja Sekar P. M. Sankaran, Praveen Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode Kerala India

ISBN: (纸本)9781665441773

Haze is a naturally occurring phenomenon that obstructs vision and affects the quality of images and videos. Recent literature has shown that deep learning-based image dehazing gives promising results both in terms of image quality and execution time. However, the difficulty of acquiring real-world hazy - clear paired images for training still remains a challenge. Widely available datasets use synthetically generated hazy images that suffer from flaws due to difficulty in acquiring accurate depth information to synthesize realistic-looking haze, causing a gap in the real and synthetic domain. In this paper, we propose the usage of domain randomization for image dehazing by generating a completely simulated training dataset for deep learning models. A standard UNET based dehazing model is trained on the simulated dataset without using any real-world data to obtain high quality dehazed images. The performance of the proposed approach is evaluated on the Sun-Dehaze dataset and RESIDE Standard (SOTS outdoor) dataset. We obtain favorable PSNR and SSIM scores on both sets and we also show how our approach yields better visual results compared to other learning-based approaches.

关键词： Deep learning Training image quality visualization Data models Standards Tuning

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Enhancing Bangla Language Text Recognition in OCR Using Levenshtein Distance and Character Grouping Strategy

Enhancing Bangla Language Text Recognition in OCR Using Leve...

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ieee International conference on Signal processing, Information, Communication & Systems (SPICSCON)

作者： Md. Sajid Hasan Avi Pal Sk. Md. Masudul Ahsan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

One crucial technology for text digitization from image is optical character recognition (OCR). Character recognition in languages with complex scripts, like Bangla, is still difficult despite recent advancements. This research proposes an innovative approach to enhance OCR's Bangla text recognition capabilities. In order to improve recognition performance, OCR errors are efficiently corrected and performance is improved by first applying the Levenshtein distance measure. Experiments show a notable improvement, increasing the initial weighted F1-score of Tesseract OCR from 84.96% to 87.26%. In addition, a method of character grouping is demonstrated to address nuances specific to the Bangla script. Characters that share visual similarities are categorized into different groupings. Subsequently, priority is given to dictionary candidate words with characters that belong to the same class as those identified by OCR. Integrating the character grouping technique yields an astounding 90.92% F1-score.

关键词： visualization Dictionaries Accuracy Text recognition Optical character recognition Measurement uncertainty Signal processing algorithms Machine learning Signal processing Linguistics

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SMCL: Saliency Masked Contrastive Learning for Long-Tailed visual Recognition

SMCL: Saliency Masked Contrastive Learning for Long-Tailed V...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Sanglee Park Seung-Won Hwang Jungmin So Sogang University Seoul Republic of Korea LG Electronics Seoul Republic of Korea Seoul National University Seoul Republic of Korea

Real-world data often follow a long-tailed distribution with a high imbalance in the number of samples between classes. The problem with training from imbalanced data is that some background features, common to all classes, can be unobserved in classes with scarce samples. As a result, this background correlates to biased predictions into "major" classes. In this paper, we propose saliency masked contrastive learning, a new method that uses saliency masking and contrastive learning to mitigate the problem and improve the generalizability of a model. Our key idea is to mask the important part of an image using saliency detection and use contrastive learning to move the masked image towards minor classes in the feature space, so that background features present in the masked image are no longer correlated with the original class. Experiment results show that our method achieves state-of-the-art level performance on benchmark long-tailed datasets.

关键词： Training Learning systems visualization Speech recognition Benchmark testing Signal processing Feature extraction

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