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SPCNet: A Panoramic image depth estimation method based on spherical convolution

SPCNet: A Panoramic image depth estimation method based on s...

引用

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

作者： He, Si Liu, Yu Wang, Yumei Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Res Ctr Networks & Commun Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728185514

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.

关键词： Depth estimation Spherical convolution Panoramic image Virtual reality

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Fully Aligned Network for Referring image Segmentation

Fully Aligned Network for Referring Image Segmentation

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2024 conference on visual communications and image processing

作者： Liu, Yong Xu, Ruihao Tang, Yansong Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

This paper focuses on the Referring image Segmentation (RIS) task, which aims to segment objects from an image based on a given language description, having significant potential in practical applications such as food safety detection. Recent advances using the attention mechanism for cross-modal interaction have achieved excellent progress. However, current methods tend to lack explicit principles of interaction design as guidelines, leading to inadequate cross-modal comprehension. Additionally, most previous works use a single-modal mask decoder for prediction, losing the advantage of full cross-modal alignment. To address these challenges, we present a Fully Aligned Network (FAN) that follows four cross-modal interaction principles. Under the guidance of reasonable rules, our FAN achieves state-of-the-art performance on the prevalent RIS benchmarks (RefCOCO, RefCOCO+, G-Ref) with a simple architecture.

关键词： image segmentation

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The Enhancement of Underexposed images with Blurred Reflectance

The Enhancement of Underexposed Images with Blurred Reflecta...

引用

ieee International conference on visual communications and image processing (VCIP)

作者： Zhou, Jinchao Wan, Renjie Li, Haoliang Kot, Alex C. Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728180687

The images captured in the low-light conditions always suffer from low visibility. Enhancing the visibility of the low-light image is of broad application to various computer vision tasks. Based on the classical Retinex model, previous methods assume the reflectance components as a well-exposed image. In this paper, we introduce the blurring distortion into the Retinex model to cover more general and challenging scenarios. We further propose a two-stage framework to extract the reflectance images and remove the blurring distortion separately. Specifically, we optimize the whole network by embedding a mechanism robust to the pixel misalignment in the training dataset. The experimental results show that our proposed method achieves promising results.

关键词： Low-light image blur illumination reflectance

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Spatio-spectral image Reconstruction Using Non-local Filtering

Spatio-spectral Image Reconstruction Using Non-local Filteri...

引用

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

作者： Sippel, Frank Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781728185514

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.

关键词： Spatio-spectral Reconstruction Error Concealment Linear Regression Block Matching Non-Local Filtering

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Fast Retrieval of Pharmaceutical Packaging images Using Keypoint Matching with Angle and Scale Voting for Outlier Rejection

Fast Retrieval of Pharmaceutical Packaging Images Using Keyp...

引用

2024 conference on visual communications and image processing

作者： Zakaria, Yona Ishiyama, Rui Ishidera, Eiki Matsui, Tomokazu Yasumoto, Keiichi Nara Inst Sci & Technol Nara Japan Univ Dodoma Dodoma Tanzania NEC Corp Ltd Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Counterfeit medicines present a severe public health threat, especially in low-resource countries where consumers lack reliable means to verify the medicines they purchase. visual inspection of medicine packaging images through keypoint matching techniques offers a promising approach for detecting design inconsistencies that could indicate counterfeit products. However, conventional methods often struggle with high computational costs and reduced accuracy when processing images of varying quality and perspectives. To address these limitations, we propose the Angle and Scale Voting (ASVote) method, which enhances keypoint-based image matching by introducing a 2D voting mechanism that leverages relative angles and scales of the keypoints to eliminate false matches(outliers) while identifying consistent matches (inliers). This approach significantly improves both processing time and accuracy. Experiments on a real-world dataset of medicine packages show that ASVote improves processing time and accuracy, outperforming conventional methods.

关键词： keypoint feature matching RANSAC USAC image retrieval visual inspection counterfeit drugs

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Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo image Super-Resolution

Two-stage Parallax Correction and Multi-stage Cross-view Fus...

引用

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

作者： Zheng, Yijian Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the two major problems of the existing methods are that the parallax correction is insufficient and the cross-view information fusion only occurs in the beginning of the network. To address these problems, we propose a two-stage parallax correction and a multi-stage cross-view fusion network for better stereo image SR results. Specially, the two-stage parallax correction module consists of horizontal parallax correction and refined parallax correction. The first stage corrects horizontal parallax by parallax attention. The second stage is based on deformable convolution to refine horizontal parallax and correct vertical parallax simultaneously. Then, multiple cascaded enhanced residual spatial feature transform blocks are developed to fuse cross-view information at multiple stages. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI2012, KITTI2015, Middlebury and Flickr1024 datasets.

关键词： Stereo image super-resolution Parallax correction Multi-stage cross-view fusion

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Frequency-aware Learned image Compression for Quality Scalability

Frequency-aware Learned Image Compression for Quality Scalab...

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ieee International conference on visual communications and image processing (VCIP)

作者： Choi, Hyomin Racape, Fabien Hamidi-Rad, Shahab Ulhaq, Mateen Feltman, Simon Interdigital Emerging Technol Lab Los Altos CA 94022 USA Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781665475921

Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.

关键词： End-to-end compression learned image compression quality scalability wavelet decomposition

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Nanopore Sequencing Simulator for DNA Data Storage

Nanopore Sequencing Simulator for DNA Data Storage

引用

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

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

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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： 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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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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