scholarly journals Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification

2021 ◽  
Author(s):  
Guoqing Zhang ◽  
Yuhao Chen ◽  
Weisi Lin ◽  
Arun Chandran ◽  
Xuan Jing
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Feature Fusion ◽  
State Of The Art ◽  
Super Resolution ◽  
Input Image ◽  
Low Resolution ◽  
Joint Learning ◽  
Novel Method ◽  
Valid Information

As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras. In unconstrained scenarios, person images often suffer from the resolution mismatch problem, i.e., Cross-Resolution Person Re-ID. To overcome this problem, most existing methods restore low resolution (LR) images to high resolution (HR) by super-resolution (SR). However, they only focus on the HR feature extraction and ignore the valid information from original LR images. In this work, we explore the influence of resolutions on feature extraction and develop a novel method for cross-resolution person re-ID called Multi-Resolution Representations Joint Learning (MRJL). Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN uses an input image to construct a HR version and a LR version with an encoder and two decoders, while the DFFN adopts a dual-branch structure to generate person representations from multi-resolution images. Comprehensive experiments on five benchmarks verify the superiority of the proposed MRJL over the relevent state-of-the-art methods.

scholarly journals An Example-Based Super-Resolution Algorithm for Selfie Images

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Jino Hans William ◽  
N. Venkateswaran ◽  
Srinath Narayanan ◽  
Sandeep Ramachandran
Keyword(s):  
High Resolution ◽  
State Of The Art ◽  
Super Resolution ◽  
Image Pair ◽  
Image Patch ◽  
Low Resolution ◽  
Pixel Resolution ◽  
Efficiency And Effectiveness ◽  
Level Information ◽  
Resolution Algorithm

A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details.

A Review of Super-Resolution Restoration from Image Sequences

2011 ◽  
Vol 204-210 ◽  
pp. 1336-1341
Author(s):  
Zhi Gang Xu ◽  
Xiu Qin Su
Keyword(s):  
High Resolution ◽  
State Of The Art ◽  
Super Resolution ◽  
Image Sequences ◽  
The State ◽  
Color Images ◽  
Low Resolution ◽  
Future Directions ◽  
High Resolution Images ◽  
Multidisciplinary Studies

Super-resolution (SR) restoration produces one or a set of high resolution images from low-resolution observations. In particular, SR restoration involves many multidisciplinary studies. A review on recent SR restoration approaches was given in this paper. First, we introduced the characteristics and framework of SR restoration. The state of the art in SR restoration was surveyed by taxonomy. Then we summarized and analyzed the existing algorithms of registration and reconstruction. A comparison of performing differences between these methods would only be valid given. After that we discussed the SR problems of color images and compressed videos. At last, we concluded with some thoughts about future directions.

scholarly journals Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2164
Author(s):  
Md. Shahinur Alam ◽  
Ki-Chul Kwon ◽  
Munkh-Uchral Erdenebat ◽  
Mohammed Y. Abbass ◽  
Md. Ashraful Alam ◽  
...  
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Three Dimensional ◽  
Super Resolution ◽  
Input Image ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Microscopic Object ◽  
Integral Imaging ◽  
Adversarial Network

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution

2021 ◽  
Vol 17 (1) ◽  
pp. 1-21
Author(s):  
Xin Jin ◽  
Jianfeng Xu ◽  
Kazuyuki Tasaka ◽  
Zhibo Chen
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Input Image ◽  
Motion Blur ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Task Learning ◽  
Degraded Image ◽  
Image Super Resolution ◽  
Low Resolution Images

In this article, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. To better share representations between multiple tasks, we propose an all-in-one collaboration framework (ACF) with a learnable “junction” unit to handle two major problems that exist in MTL—“How to share” and “How much to share.” Specifically, ACF consists of a sharing phase and a reconstruction phase. Considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur, and spatial structure information of the input image in parallel under a three-branch architecture in the sharing phase. Subsequently, in the reconstruction phase, we up-sample the previous features for high-resolution image reconstruction with a channel-wise and spatial attention mechanism. To coordinate two phases, we introduce a learnable “junction” unit with a dual-voting mechanism to selectively filter or preserve shared feature representations that come from sharing phase, learning an optimal combination for the following reconstruction phase. Finally, a curriculum learning-based training scheme is further proposed to improve the convergence of the whole framework. Extensive experimental results on synthetic and real-world low-resolution images show that the proposed all-in-one collaboration framework not only produces favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods. We also have applied ACF to some image-quality sensitive practical task, such as pose estimation, to improve estimation accuracy of low-resolution images.

scholarly journals Resolution-invariant Person Re-Identification

2019 ◽  
Author(s):  
Shunan Mao ◽  
Shiliang Zhang ◽  
Ming Yang
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Large Range ◽  
State Of The Art ◽  
Super Resolution ◽  
The State ◽  
Invariant Representation ◽  
Invariant Feature ◽  
Feature Extractor ◽  
High Resolution Images

Exploiting resolution invariant representation is critical for person Re-Identification (ReID) in real applications, where the resolutions of captured person images may vary dramatically. This paper learns person representations robust to resolution variance through jointly training a Foreground-Focus Super-Resolution (FFSR) module and a Resolution-Invariant Feature Extractor (RIFE) by end-to-end CNN learning. FFSR upscales the person foreground using a fully convolutional auto-encoder with skip connections learned with a foreground focus training loss. RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively. These two complementary modules are jointly trained, leading to a strong resolution invariant representation. We evaluate our methods on five datasets containing person images at a large range of resolutions, where our methods show substantial superiority to existing solutions. For instance, we achieve Rank-1 accuracy of 36.4% and 73.3% on CAVIAR and MLR-CUHK03, outperforming the state-of-the art by 2.9% and 2.6%, respectively.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

scholarly journals MR-InpaintNet: Toward Deep Multi-Resolution Learning for Progressive Image Inpainting

2021 ◽  
Author(s):  
Huan Zhang ◽  
Zhao Zhang ◽  
Haijun Zhang ◽  
Yi Yang ◽  
Shuicheng Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Semantic Information ◽  
Feature Fusion ◽  
Image Inpainting ◽  
Feature Learning ◽  
Low Resolution ◽  
Resolution Image ◽  
Texture Information ◽  
Multiple Resolutions

<div>Deep learning based image inpainting methods have improved the performance greatly due to powerful representation ability of deep learning. However, current deep inpainting methods still tend to produce unreasonable structure and blurry texture, implying that image inpainting is still a challenging topic due to the ill-posed property of the task. To address these issues, we propose a novel deep multi-resolution learning-based progressive image inpainting method, termed MR-InpaintNet, which takes the damaged images of different resolutions as input and then fuses the multi-resolution features for repairing the damaged images. The idea is motivated by the fact that images of different resolutions can provide different levels of feature information. Specifically, the low-resolution image provides strong semantic information and the high-resolution image offers detailed texture information. The middle-resolution image can be used to reduce the gap between low-resolution and high-resolution images, which can further refine the inpainting result. To fuse and improve the multi-resolution features, a novel multi-resolution feature learning (MRFL) process is designed, which is consisted of a multi-resolution feature fusion (MRFF) module, an adaptive feature enhancement (AFE) module and a memory enhanced mechanism (MEM) module for information preservation. Then, the refined multi-resolution features contain both rich semantic information and detailed texture information from multiple resolutions. We further handle the refined multiresolution features by the decoder to obtain the recovered image. Extensive experiments on the Paris Street View, Places2 and CelebA-HQ datasets demonstrate that our proposed MRInpaintNet can effectively recover the textures and structures, and performs favorably against state-of-the-art methods.</div>

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

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