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DIRECT: Discrete image Rescaling with Enhancement from Case-specific Textures

DIRECT: Discrete Image Rescaling with Enhancement from Case-...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Yan-An Chen Ching-Chun Hsiao Wen-Hsiao Peng Ching-Chun Huang Natl Yang Ming Chiao Tung Univ Inst Comp Sci & Engn Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728185514

This paper addresses image resealing, the task of which is to downscale an input image followed by upscaling for the purposes of transmission, storage, or playback on heterogeneous devices. The state-of-the-art image resealing network (known as IRN) tackles image downscaling and upscaling as mutually invertible tasks using invertible affine coupling layers. In particular, for upscaling, IRN models the missing high-frequency component by an input-independent (case-agnostic) Gaussian noise. In this work, we take one step further to predict a case-specific high-frequency component from textures embedded in the downscaled image. Moreover, we adopt integer coupling layers to avoid quantizing the downscaled image. When tested on commonly used datasets, the proposed method, termed DIRECT, improves high-resolution reconstruction quality both subjectively and objectively, while maintaining visually pleasing downscaled images.

关键词： Single image Resealing Discrete Invertible Neural Network Case-specific network

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Deep Learning-Based Blind image Super-Resolution using Iterative Networks

Deep Learning-Based Blind Image Super-Resolution using Itera...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Yaar, Asfand Ates, Hasan F. Gunturk, Bahadir K. Istanbul Medipol Univ Sch Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781728185514

Deep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, performance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation;ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.

关键词： Super-resolution Kernel estimation Blind Iterative Deep learning

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An Error Self-learning Semi-supervised Method for No-reference image Quality Assessment

An Error Self-learning Semi-supervised Method for No-referen...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Feng, Yingjie Li, Sumei Hao, Sihan Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

关键词： error self-learning semi-supervised no-reference image quality assessment

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Nanopore Sequencing Simulator for DNA Data Storage

Nanopore Sequencing Simulator for DNA Data Storage

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： San Antonio, Eva Gil Heinis, Thomas Carteron, Louis Dimopoulou, Melpomeni Antonini, Marc Univ Cote Azur CNRS Lab I3S UMR 7271 Sophia Antipolis France Imperial Coll London Dept Comp London England

ISBN: (纸本)9781728185514

The exponential increase of digital data and the limited capacity of current storage devices have made clear the need for exploring new storage solutions. Thanks to its biological properties, DNA has proven to be a potential candidate for this task, allowing the storage of information at a high density for hundreds or even thousands of years. With the release of nanopore sequencing technologies, DNA data storage is one step closer to become a reality. Many works have proposed solutions for the simulation of this sequencing step, aiming to ease the development of algorithms addressing nanopore-sequenced reads. However, these simulators target the sequencing of complete genomes, whose characteristics differ from the ones of synthetic DNA. This work presents a nanopore sequencing simulator targeting synthetic DNA on the context of DNA data storage.

关键词： DNA data storage nanopore sequencing sequencing simulator image coding

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DCTResNet: Transform Domain image Deblocking for Motion Blur images

DCTResNet: Transform Domain Image Deblocking for Motion Blur...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Maharjan, Paras Xu, Ning Xu, Xuan Song, Yuyan Li, Zhu Univ Missouri Kansas City MO 64110 USA Kwai Inc Palo Alto CA USA

ISBN: (纸本)9781728185514

Pixel recovery with deep learning has shown to be very effective for a variety of low-level vision tasks like image super-resolution, denoising, and deblurring. Most existing works operate in the spatial domain, and there are few works that exploit the transform domain for image restoration tasks. In this paper, we present a transform domain approach for image deblocking using a deep neural network called DCTResNet. Our application is compressed video motion deblur, where the input video frame has blocking artifacts that make the deblurring task very challenging. Specifically, we use a block-wise Discrete Cosine Transform (DCT) to decompose the image into its low and high-frequency sub-band images and exploit the strong subband specific features for more effective deblocking solutions. Since JPEG also uses DCT for image compression, using DCT sub-band images for image deblocking helps to learn the JPEG compression prior to effectively correct the blocking artifacts. Our experimental results show that both PSNR and SSIM for DCTResNet perform more favorably than other state-of-the-art (SOTA) methods, while significantly faster in inference time.

关键词： Discrete cosine transforms

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Two Stage image Restoration based on Histogram Equalization and Edge Computation for image 1

Two Stage Image Restoration based on Histogram Equalization ...

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1st International conference on Computer, Power and communications, ICCPC 2022

作者： Kumar, V. Senthil Jayalakshmi, V. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Science Chennai India

ISBN: (纸本)9798350397840

image restoration is the basic technology in image processing to increase the contrast and brightness of the image. Also, this technique should help to characterize the content of an image with high efficiency. It is widely used in many applications. This paper introduces the potential of the two stages functional in an image for image restoration. In the first stage, pair wise pixel histogram equalization is introduced to enhance the image content. For the betterment in the visual quality of foreground region present in the image, the image are analysed by finding edges of the object in the second stage. Also, markov chain transition is used for efficient edge detection. © 2022 IEEE.

关键词： image reconstruction

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An Alternate Approach for Single image Haze Removal Using Path Prediction 7th

An Alternate Approach for Single Image Haze Removal Using Pa...

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7th International conference on Computer Vision and image processing, CVIP 2022

作者： Agarwal, Divyansh Rajput, Amitesh Singh Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9783031314063

Haze removal is an important problem that existing studies have addressed from slight to extreme levels. It finds wide application in landscape photography where the haze causes low contrast and saturation, but it can also be used to improve images taken during rainy and foggy conditions. In this paper, considering the importance of haze removal and possible limitations of a well-known existing method, DehazeNet, we propose an alternate end-to-end method for single image haze removal using simple yet efficient image processing techniques. DehazeNet is among well performing haze removal schemes in the literature, however the problem of coloration and artifacts being produced in the output images has been observed for a certain set of images. Addressing this problem, the proposed solution is devised by taking advantage of the color features of the three color channels of an image to establish a decision criteria. Based on that, a suitable dehazing method for an input hazy image is selected. Removing the problem of poor coloration in output images, we have come up with an alter-nate method to remove the haze while retaining the visual and perceived quality of the image. The experimental results show that the proposed method yields better structural restoration, reduces haze content signif-icantly, and does not cause any artifacts in the output image. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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Utilizing image processing Techniques to Develop a Novel Scoring System for Corn Ear Shape 37

Utilizing Image Processing Techniques to Develop a Novel Sco...

引用

37th International Technical conference on Circuits/Systems, Computers and communications (ITC-CSCC)

作者： Yawichai, Amika Cline, Eliot Khunlertgit, Navadon Chiang Mai Univ OASYS Res Grp Dept Comp Engn Fac Engn Chiang Mai Thailand Hortigenet Res SE Asia Ltd Chiang Mai Thailand Chiang Mai Univ Biomed Engn Inst Chiang Mai Thailand

ISBN: (数字)9781665485593

ISBN: (纸本)9781665485593

Shape is one of the features that can attract sweet corn consumers. Moreover, the proper shape allows for more efficient processing in the industry. According to the information we have, there is currently no systematic approach for corn ear shape scoring. For this reason, we developed a new corn ear shape scoring system by utilizing image processing techniques. We compared the results of our proposed scoring with the results of a visual inspection by corn experts. The results showed that our scoring system was able to consistently group corn images by shape, with less variation within each group. As a result, our proposed scoring system can be used to establish ear shape standards and phenotyping in corn breeding programs.

关键词： Corn ear shape scoring image processing image analysis scoring design

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Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy image Segmentation 5

Adaptive Kernelized Evidence C-Means Clustering Combining Sp...

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5th International conference on Natural Language processing, ICNLP 2023

作者： Rong, Lan Haowen, Mi Na, Qu Feng, Zhao Haiyan, Yu Lu, Zhang Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9798350302219

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation. © 2023 IEEE.

关键词： image segmentation

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Generative DNA: Representation Learning for DNA-based Approximate image Storage

Generative DNA: Representation Learning for DNA-based Approx...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Franzese, Giulio Yan, Yiqing Serra, Giuseppe D'Onofrio, Ivan Appuswamy, Raja Michiardi, Pietro EURECOM Dept Data Sci Biot France

ISBN: (纸本)9781728185514

Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neural-network-based compression techniques, we transform images into a latent-space representation, which we then store on DNA. By doing so, we transform DNA into an approximate image storage medium, as images generated back from DNA are only approximate representations of the original images. Using several datasets, we investigate the storage benefits of approximation, and study the impact of DNA storage errors (substitutions, indels, bias) on the quality of approximation. In doing so, we demonstrate the feasibility and potential of viewing DNA as an approximate storage medium.

关键词： DNA storage representation learning approximate storage compression

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