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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Li, Taihui Lahiri, Anish Dai, Yutong Mayer, Owen Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Sony Corp Amer R&D US Lab San Jose CA USA

ISBN: (纸本)9798350344868;9798350344851

Demosaicing and denoising of RAW images are crucial steps in the image signal processing pipeline of modern digital cameras. As only a third of the color information required to produce a digital image is captured by the camera sensor, the process of demosaicing is inherently ill-posed. The presence of noise further exacerbates this problem. Performing these two steps sequentially may distort the content of the captured RAWimages and accumulate errors from one step to another. Recent deep neural-network-based approaches have shown the effectiveness of joint demosaicing and denoising to mitigate such challenges. However, these methods typically require a large number of training samples and do not generalize well to different types and intensities of noise. In this paper, we propose a novel joint demosaicing and denoising method, dubbed JDD-DoubleDIP, which operates directly on a single RAW image without requiring any training data. We validate the effectiveness of our method on two popular datasets-Kodak and McMaster-with various noises and noise intensities. The experimental results show that our method consistently outperforms other compared methods in terms of PSNR, SSIM, and qualitative visual perception.

关键词： image Signal processing Deep image Prior RAW images Demosaicing Denoising

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Research on the Construction of image processing Model Based on Computer Vision Algorithm 2

Research on the Construction of Image Processing Model Based...

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2nd ieee International conference on Control, Electronics and Computer Technology, ICCECT 2024

作者： Yi, Wang School of Law Shandong University of Technology Economic Law Shandong Zibo255000 China

ISBN: (纸本)9798350380958

This research focuses on constructing an efficient image processing model, which is rooted in computer vision algorithms, to ameliorate image distortion and optimize visual display systems. The article initially discusses the significance of image distortion correction and its processing flow, encompassing detailed steps ranging from image preprocessing, model construction, parameter estimation, to pixel coordinate transformation. Furthermore, the design approach of computer vision display systems is explored, covering the theory of light field reconstruction, software and hardware design essentials, ensuring high fidelity in image presentation. Through multiple experiments conducted on datasets of distorted images, the effectiveness of the model in correcting distortion and enhancing image quality is empirically demonstrated, with the experimental results strongly supporting the feasibility and superiority of the model. This research holds positive implications for advancements and practical applications in the field of image processing. © 2024 ieee.

关键词： image enhancement

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Construction of a Plane visual Art Design System based on Human Computer Interaction and Digital image processing Technology 2

Construction of a Plane Visual Art Design System based on Hu...

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2nd ieee International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Feng, Jiahui Wang, Mengyao Che, Zuping Xiang, Ci College of Art and Design Wuhan Textile University Hubei Wuhan China

ISBN: (纸本)9798331504960

This study proposes a graphic visual art design system based on human-computer interaction and digital image processing technology to address the problems of traditional graphic visual art design methods being time-consuming, prone to errors, and limiting creative freedom of expression. Firstly, we conducted a user needs analysis to understand the pain points and requirements that designers encounter in their daily work;Subsequently, the image processing algorithm based on Convolutional Neural Network (CNN) was optimized to improve the speed and quality of image processing;Then, an intuitive human-computer interaction interface was designed to simplify the operation process and provide real-time preview functionality;Finally, integrate each module into the system to ensure efficient collaboration of data and functionality. The experimental results show that compared with traditional tools, using this system to complete design tasks has shortened the average time by nearly 20 minutes, improved the design quality score by about 0.75 points, and significantly increased user satisfaction. These results indicate that the system proposed in this study has significant advantages in improving design efficiency and quality, effectively addressing the limitations of traditional methods and providing designers with more efficient and flexible design tools. © 2024 ieee.

关键词： Convolutional neural networks

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A Fully Automatic Colorimetric Saliency Detection Approach for 3D Meshes 12

A Fully Automatic Colorimetric Saliency Detection Approach f...

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12th ieee International Symposium on Signal, image, Video, and communications, ISIVC 2024

作者： Nouri, Anass Lezoray, Olivier Ibork, Zaineb Charrier, Christophe Touahni, Raja Ibn Tofail University Setime Laboratory Information Processing and A.I Team Ensc Faculty of Science Kénitra Morocco Normandie Univ Unicaen Ensicaen Cnrs Greyc Caen France Ibn Tofail University Setime Laboratory Information Processing and A.I Team Faculty of Science Kenitra Morocco

ISBN: (纸本)9798350385267

With the democratization of acquisition systems, 3D meshes have established themselves as the most optimal modality for representing, analyzing, transmitting, editing and printing 3D contents. All these processes have one thing in common: the human observers who visualize the 3D content downstream. To optimize these different processes, it would be judicious and relevant to take advantage of a characteristic present in all human visual systems, namely the sensitivity to visual saliency. In the literature, very little work has been carried out on predicting visual saliency on colored meshes, as opposed to uncolored meshes. Yet the colorimetric aspect of visual content (whether 2D or 3D, static or moving) plays a key role in directing the human visual attention. In this paper, we propose a new approach, requiring no intervention, parameterization or learning, for the detection of visual saliency of 3D colored meshes. The results demonstrate the relevance and the superiority of the proposed approach. © 2024 ieee.

关键词： Colorimetry

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RESTORATION OF EXTREMELY COMPRESSED BACKGROUND FOR VCM USING GUIDED GENERATIVE PRIORS 30

RESTORATION OF EXTREMELY COMPRESSED BACKGROUND FOR VCM USING...

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30th ieee International conference on image processing (ICIP)

作者： Dao, Le Thi Hue Vien, An Gia Lee, Jooyoung Jeong, Seyoon Lee, Chul Dongguk Univ Dept Multimedia Engn Seoul South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781728198354

We propose a learning-based image restoration algorithm for a single decoded image with a high-quality foreground and an extremely degraded background for video coding for machines (VCM). First, we develop an encoder that extracts multiscale features and learns latent vectors. Then, a background generator with style and feature fusion blocks generates guided features that contain the prior background information in the input image. Finally, the decoder restores the degraded background region by merging the image features from the encoder and prior background information from the generator. Experimental results show that the proposed algorithm achieves better performance than state-of-the-art algorithms.

关键词： image generation image restoration video coding for machines (VCM)

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A Deep Reinforcement Learning-Based Beam Prediction Scheme for Vision-Aided mmWave Wireless communications

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ieee Internet of Things Journal 2025年第11期12卷 17869-17879页

作者： Wang, Heng Yang, Dihan Xie, Xin The Key Laboratory of Industrial Internet of Things and Networked Control Chongqing University of Posts and Telecommunications Chongqing400065 China

Millimeter wave (mmWave) wireless communications are significant technologies that support Internet of Things (IoT) systems to achieve fast and stable data transmission, and the guarantee of its communication quality usually depends on accurate beam prediction. Due to the advantage of not relying on channel state information, beam prediction schemes using visual data and artificial intelligence become popular. Most of the current vision-aided beam prediction schemes directly predict the index of the optimal beam in the codebook. However, these methods are only applicable to certain codebooks and have limited generalization and scalability. To address the issues, we propose a vision-aided beam prediction scheme based on deep reinforcement learning (DRL). The scheme takes the original image as input and extracts the position and velocity of the user through the object detection algorithm. Subsequently, combined with the current state of the base station, it outputs a continuous angle value and finally matches it with the beam index in the codebook. Furthermore, we integrate the attention mechanism into the actor network of the deep deterministic policy gradient (DDPG) and propose a scheme of DDPG with attention mechanism (DDPG-A), which can perform differential processing on features, thereby enhancing algorithmic performance. The simulation test utilizing real datasets demonstrates that the proposed scheme has good generalization and scalability while considerably reducing the beam training overheads. © 2014 ieee.

关键词： Deep reinforcement learning

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JPEG INFORMATION REGULARIZED DEEP image PRIOR FOR DENOISING 30

JPEG INFORMATION REGULARIZED DEEP IMAGE PRIOR FOR DENOISING

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30th ieee International conference on image processing (ICIP)

作者： Takagi, Tsukasa Ishizaki, Shinya Maeda, Shin-ichi Preferred Networks Inc Tokyo Japan Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9781728198354

image denoising is a representative image restoration task in computer vision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful image denoising from only a noisy image by inductive bias of convolutional neural network architectures without any pre-training. The major challenge of DIP based image denoising is that DIP would completely recover the original noisy image unless applying early stopping. For early stopping without a ground-truth clean image, we propose to monitor JPEG file size of the recovered image during optimization as a proxy metric of noise levels in the recovered image. Our experiments show that the compressed image file size works as an effective metric for early stopping.

关键词： image Denoising Deep image Prior JPEG Compression Early Stopping

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Composable image Coding for Machine via Task-oriented Internal Adaptor and External Prior

Composable Image Coding for Machine via Task-oriented Intern...

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2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Liu, Jinming Jin, Xin Feng, Ruoyu Chen, Zhibo Zeng, Wenjun Shanghai Jiao Tong University China Eastern Institute of Technology Ningbo China Ningbo Institute of Digital Twin China University of Science and Technology of China China

ISBN: (纸本)9798350359855

Traditional image coding standards are typically optimized with a focus on human perception, which conflicts with the fact that most of the images are now analyzed by machines. To enable a variety of downstream intelligent tasks, contemporary approaches either utilize traditional codecs for image compression which are then used for task analysis, or develop a unified feature compression paradigm with deep learning techniques. However, they might suffer from accumulative errors and poor compatibility/generalization due to the conflict between standardized codecs and diverse machine tasks. We argue that a favorable image coding for machine (ICM) framework should have highly efficient adaptation capability, and take the ultimate task goals into account. Oriented at this, we propose a composable ICM solution dubbed Com-ICM, which develops plug-And-play lightweight internal adaptors injected into the codec architecture for efficient task transfer, and leverages off-The-shelf (large) models to provide external prior information for further task-oriented semantics learning. The internal adaptors (from the architectural aspect) and external priors (from the precondition aspect) complement each other, resulting in a mutually beneficial effect. We evaluate Com-ICM on diverse vision benchmarks, including image classification, object detection, and semantic segmentation, demonstrating its effectiveness and superiority. We are also actively submitting Com-ICM as a technical proposal to the international organization for standardization. © 2023 ieee.

关键词： image compression

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PRE-TRAINING WITH FRACTAL imageS FACILITATES LEARNED image QUALITY ESTIMATION 30

PRE-TRAINING WITH FRACTAL IMAGES FACILITATES LEARNED IMAGE Q...

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30th ieee International conference on image processing (ICIP)

作者： Silbernagel, Malte Wiegand, Thomas Eisert, Peter Bosse, Sebastian Fraunhofer HHI Berlin Germany

ISBN: (纸本)9781728198354

Today's image quality estimation is widely dominated by learning-based approaches. The availability of annotated, i.e. rated, images is often a bottleneck in training datadriven visual quality models and hinders their generalization power. This paper proposed a novel pre-training scheme for learning-based quality estimation that does not rely on human-annotated datasets, but leverages synthetic fractal images. These images can be synthesized inexhaustibly and are inherently labeled during generation. We evaluate the pre-training strategy on a popular neural network-based quality model and show that the training effort can be reduced significantly, resulting in better final accuracy and faster convergence speed.

关键词： neural networks pre-training image quality estimation perception models fractals

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REDEFINING NIGHT VISION: THE POWER OF MSR-DRIVEN NEURAL ISP 49

REDEFINING NIGHT VISION: THE POWER OF MSR-DRIVEN NEURAL ISP

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hou, Jingchao He, Guanghui Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced MultiScale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, regionspecific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.

关键词： image Signal processing (ISP) Multi-Scale Retinex (MSR) Enhancement Reflection and Illumination Separation

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