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Author Eduard Vazquez; Ramon Baldrich
Title Non-supervised goodness measure for image segmentation Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal
Volume Issue Pages 334–335
Keywords
Abstract
Address Gjovik, Norway
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CREATE
Notes CIC Approved no
Call Number CAT @ cat @ VaB2010 Serial 1299
Permanent link to this record
 
 
Author Jaime Moreno; Xavier Otazu; Maria Vanrell
Title Contribution of CIWaM in JPEG2000 Quantization for Color Images Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal
Volume Issue Pages 132–136
Keywords
Abstract The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM(ChromaticInductionWaveletModel).
Address Gjovik (Norway)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CREATE
Notes CIC Approved no
Call Number CAT @ cat @ MOV2010b Serial 1308
Permanent link to this record
 
 
Author Javier Vazquez; Maria Vanrell; Robert Benavente
Title Color names as a constraint for Computer Vision problems Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal
Volume Issue Pages 324–328
Keywords
Abstract Computer Vision Problems are usually ill-posed. Constraining de gamut of possible solutions is then a necessary step. Many constrains for different problems have been developed during years. In this paper, we present a different way of constraining some of these problems: the use of color names. In particular, we will focus on segmentation, representation ans constancy.
Address Gjovik (Norway)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CREATE
Notes CIC Approved no
Call Number CAT @ cat @ VVB2010 Serial 1328
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title Who Painted this Painting? Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal
Volume Issue Pages 329–333
Keywords
Abstract
Address Gjovik (Norway)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CREATE
Notes CIC Approved no
Call Number CAT @ cat @ KWV2010 Serial 1329
Permanent link to this record
 
 
Author Shida Beigpour; Joost Van de Weijer
Title Photo-Realistic Color Alteration for Architecture and Design Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal
Volume Issue Pages 84–88
Keywords
Abstract As color is a strong stimuli we receive from the exterior world, choosing the right color can prove crucial in creating the desired architecture and desing. We propose a framework to apply a realistic color change on both objects and their illuminant lights for snapshots of architectural designs, in order to visualize and choose the right color before actully applying the change in the real world. The proposed framework is based on the laws of physics in order to accomplish realistic and physically plausible results.
Address Gjovik (Norway)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CREATE
Notes CIC Approved no
Call Number CAT @ cat @ BeW2010 Serial 1330
Permanent link to this record
 
 
Author Robert Benavente; Laura Igual; Fernando Vilariño
Title Current Challenges in Computer Vision Type Book Whole
Year 2008 Publication Proccedings of the Third Internal Workshop Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-936529-0-6 Medium
Area Expedition Conference (up) CVCRD
Notes MILAB;CIC;SIAI Approved no
Call Number BCNPCL @ bcnpcl @ BIV2008 Serial 1110
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Author Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez
Title Harmony Potentials for Joint Classification and Segmentation Type Conference Article
Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 3280–3287
Keywords
Abstract Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21.
Address San Francisco CA, USA
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN 978-1-4244-6984-0 Medium
Area Expedition Conference (up) CVPR
Notes ADAS;CIC;ISE Approved no
Call Number ADAS @ adas @ GBW2010 Serial 1296
Permanent link to this record
 
 
Author Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga
Title Saliency Estimation Using a Non-Parametric Low-Level Vision Model Type Conference Article
Year 2011 Publication IEEE conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 433-440
Keywords Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms
Abstract Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.
Address Colorado Springs
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN 978-1-4577-0394-2 Medium
Area Expedition Conference (up) CVPR
Notes CIC Approved no
Call Number Admin @ si @ MVO2011 Serial 1757
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez
Title Color Attributes for Object Detection Type Conference Article
Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 3306-3313
Keywords pedestrian detection
Abstract State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.
In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-
art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.
Address Providence; Rhode Island; USA;
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium
Area Expedition Conference (up) CVPR
Notes ADAS; CIC; Approved no
Call Number Admin @ si @ KRW2012 Serial 1935
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Author Naila Murray; Luca Marchesotti; Florent Perronnin
Title AVA: A Large-Scale Database for Aesthetic Visual Analysis Type Conference Article
Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 2408-2415
Keywords
Abstract With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks
Address Providence, Rhode Islan
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium
Area Expedition Conference (up) CVPR
Notes CIC Approved no
Call Number Admin @ si @ MMP2012a Serial 2025
Permanent link to this record
 
 
Author Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell
Title Names and Shades of Color for Intrinsic Image Estimation Type Conference Article
Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 278-285
Keywords
Abstract In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance.
Address Providence, Rhode Island
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium
Area Expedition Conference (up) CVPR
Notes CIC Approved no
Call Number Admin @ si @ SPB2012 Serial 2026
Permanent link to this record
 
 
Author Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat
Title Discriminative Color Descriptors Type Conference Article
Year 2013 Publication IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 2866 - 2873
Keywords
Abstract Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.
Address Portland; Oregon; June 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN Medium
Area Expedition Conference (up) CVPR
Notes CIC; 600.048 Approved no
Call Number Admin @ si @ KWK2013a Serial 2262
Permanent link to this record
 
 
Author Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras
Title The Photometry of Intrinsic Images Type Conference Article
Year 2014 Publication 27th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 1494-1501
Keywords
Abstract Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images.
Address Columbus; Ohio; USA; June 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CVPR
Notes CIC; 600.052; 600.051; 600.074 Approved no
Call Number Admin @ si @ SPB2014 Serial 2506
Permanent link to this record
 
 
Author M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer
Title Adaptive color attributes for real-time visual tracking Type Conference Article
Year 2014 Publication 27th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 1090 - 1097
Keywords
Abstract Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second.
Address Nottingham; UK; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CVPR
Notes CIC; LAMP; 600.074; 600.079 Approved no
Call Number Admin @ si @ DKF2014 Serial 2509
Permanent link to this record
 
 
Author Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde
Title Towards a Perceptual Evaluation Framework for Lighting Estimation Type Conference Article
Year 2024 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms.
Address Seattle; USA; June 2024
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CVPR
Notes MACO; CIC Approved no
Call Number Admin @ si @ GDH2024 Serial 3999
Permanent link to this record
 
 
Author Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta
Title Area Under the ROC Curve Maximization for Metric Learning Type Conference Article
Year 2022 Publication CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) Abbreviated Journal
Volume Issue Pages
Keywords Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition
Abstract Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.
Address New Orleans, USA; 20 June 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CVPRW
Notes CIC; LAMP; Approved no
Call Number Admin @ si @ GAB2022 Serial 3700
Permanent link to this record
 
 
Author Bojana Gajic; Ramon Baldrich
Title Cross-domain fashion image retrieval Type Conference Article
Year 2018 Publication CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) Abbreviated Journal
Volume Issue Pages 19500-19502
Keywords
Abstract Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.
Address Salt Lake City, USA; 22 June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CVPRW
Notes CIC; 600.087 Approved no
Call Number Admin @ si @ Serial 3709
Permanent link to this record
 
 
Author Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang
Title NTIRE 2023 challenge on image denoising: Methods and results Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages 1904-1920
Keywords
Abstract This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising.
Address Vancouver; Canada; June 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) CVPRW
Notes MACO; CIC Approved no
Call Number Admin @ si @ LZT2023 Serial 3910
Permanent link to this record