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Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
Title	Perceptual color texture codebooks for retrieving in highly diverse texture datasets			Type	Conference Article
Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	866–869
Keywords
Abstract	Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel.
Address	Istanbul (Turkey)
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	1051-4651	ISBN	978-1-4244-7542-1	Medium
Area		Expedition		Conference	ICPR
Notes	CIC			Approved	no
Call Number	CAT @ cat @ ASV2010b			Serial	1426
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Author	Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva
Title	Colour Normalisation Based on Background Information.			Type	Miscellaneous
Year	2001	Publication	Proceeding ICIP 2001, IEEE International Conference on Image Processing	Abbreviated Journal	ICIP 2001
Volume		Issue	1	Pages	874–877
Keywords
Abstract
Address	Grecia.
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
Notes	ADAS;DAG;CIC			Approved	no
Call Number	ADAS @ adas @ VLP2001			Serial	167
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Author	Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell
Title	Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures			Type	Journal Article
Year	2011	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
Volume	33	Issue	5	Pages	917-930
Keywords
Abstract	The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost.
Address	Los Alamitos; CA; USA;
Corporate Author				Thesis
Publisher	IEEE Computer Society	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	0162-8828	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ VBW2011			Serial	1715
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Author	Arjan Gijsenij; Theo Gevers; Joost Van de Weijer
Title	Improving Color Constancy by Photometric Edge Weighting			Type	Journal Article
Year	2012	Publication	IEEE Transaction on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
Volume	34	Issue	5	Pages	918-929
Keywords
Abstract	: Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy.
Address	Los Alamitos; 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	0162-8828	ISBN		Medium
Area		Expedition		Conference
Notes	CIC;ISE			Approved	no
Call Number	Admin @ si @ GGW2012			Serial	1850
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Author	Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title	Top-Down Color Attention for Object Recognition			Type	Conference Article
Year	2009	Publication	12th International Conference on Computer Vision	Abbreviated Journal
Volume		Issue		Pages	979 - 986
Keywords
Abstract	Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
Address	Kyoto, Japan
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	1550-5499	ISBN	978-1-4244-4420-5	Medium
Area		Expedition		Conference	ICCV
Notes	CIC			Approved	no
Call Number	CAT @ cat @ SWV2009			Serial	1196
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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	CVPR
Notes	CIC; LAMP; 600.074; 600.079			Approved	no
Call Number	Admin @ si @ DKF2014			Serial	2509
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Author	Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich
Title	Illuminant Invariant Model-Based Road Segmentation			Type	Conference Article
Year	2008	Publication	IEEE Intelligent Vehicles Symposium,	Abbreviated Journal
Volume		Issue		Pages	1155–1180
Keywords	road detection
Abstract
Address	Eindhoven (The Netherlands)
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
Notes	ADAS;CIC			Approved	no
Call Number	ADAS @ adas @ ALB2008			Serial	1045
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Author	Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell
Title	Spectral sharpening by spherical sampling			Type	Journal Article
Year	2012	Publication	Journal of the Optical Society of America A	Abbreviated Journal	JOSA A
Volume	29	Issue	7	Pages	1199-1210
Keywords
Abstract	There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art.
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	1084-7529	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ FVS2012			Serial	2000
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Author	Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title	Brightness and colour induction through contextual influences in V1			Type	Conference Article
Year	2015	Publication	Scottish Vision Group 2015 SGV2015	Abbreviated Journal
Volume	12	Issue	9	Pages	1208-2012
Keywords
Abstract
Address	Carnoustie; Scotland; March 2015
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	SGV
Notes	NEUROBIT;CIC			Approved	no
Call Number	Admin @ si @ OPC2015a			Serial	2632
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Author	Christophe Rigaud; Dimosthenis Karatzas; Joost Van de Weijer; Jean-Christophe Burie; Jean-Marc Ogier
Title	An active contour model for speech balloon detection in comics			Type	Conference Article
Year	2013	Publication	12th International Conference on Document Analysis and Recognition	Abbreviated Journal
Volume		Issue		Pages	1240-1244
Keywords
Abstract	Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent comic book understanding would enable a variety of new applications, including content-based retrieval and content retargeting. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. Few studies have been done in this direction. In this work we detail a novel approach for closed and non-closed speech balloon localization in scanned comic book pages, an essential step towards a fully automatic comic book understanding. The approach is compared with existing methods for closed balloon localization found in the literature and results are presented.
Address	washington; USA; August 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	1520-5363	ISBN		Medium
Area		Expedition		Conference	ICDAR
Notes	DAG; CIC; 600.056			Approved	no
Call Number	Admin @ si @ RKW2013a			Serial	2260
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Author	Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg
Title	Painting-91: A Large Scale Database for Computational Painting Categorization			Type	Journal Article
Year	2014	Publication	Machine Vision and Applications	Abbreviated Journal	MVAP
Volume	25	Issue	6	Pages	1385-1397
Keywords
Abstract	Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.
Address
Corporate Author				Thesis
Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	0932-8092	ISBN		Medium
Area		Expedition		Conference
Notes	CIC; LAMP; 600.074; 600.079			Approved	no
Call Number	Admin @ si @ KBW2014			Serial	2510
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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	CVPR
Notes	CIC; 600.052; 600.051; 600.074			Approved	no
Call Number	Admin @ si @ SPB2014			Serial	2506
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Author	Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg
Title	Scale Coding Bag-of-Words for Action Recognition			Type	Conference Article
Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	1514-1519
Keywords
Abstract	Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image. Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.
Address	Stockholm; August 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	ICPR
Notes	CIC; LAMP; 601.240; 600.074; 600.079			Approved	no
Call Number	Admin @ si @ KWB2014			Serial	2450
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Author	Xavier Otazu; Oriol Pujol
Title	Wavelet based approach to cluster analysis. Application on low dimensional data sets			Type	Journal Article
Year	2006	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
Volume	27	Issue	14	Pages	1590–1605
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		Medium
Area		Expedition		Conference
Notes	MILAB; CIC; HuPBA			Approved	no
Call Number	BCNPCL @ bcnpcl @ OtP2006			Serial	658
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Author	Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez
Title	Discriminative Compact Pyramids for Object and Scene Recognition			Type	Journal Article
Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
Volume	45	Issue	4	Pages	1627-1636
Keywords
Abstract	Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.
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	0031-3203	ISBN		Medium
Area		Expedition		Conference
Notes	ISE; CAT;CIC			Approved	no
Call Number	Admin @ si @ EKW2012			Serial	1807
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Author	O. Fors; J. Nuñez; Xavier Otazu; A. Prades; Robert D. Cardinal
Title	Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques			Type	Journal Article
Year	2010	Publication	Sensors	Abbreviated Journal	SENS
Volume	10	Issue	3	Pages	1743–1752
Keywords	image processing; image deconvolution; faint stars; space debris; wavelet transform
Abstract	Abstract: In this paper we show how the techniques of image deconvolution can increase the ability of image sensors as, for example, CCD imagers, to detect faint stars or faint orbital objects (small satellites and space debris). In the case of faint stars, we show that this benefit is equivalent to double the quantum efficiency of the used image sensor or to increase the effective telescope aperture by more than 30% without decreasing the astrometric precision or introducing artificial bias. In the case of orbital objects, the deconvolution technique can double the signal-to-noise ratio of the image, which helps to discover and control dangerous objects as space debris or lost satellites. The benefits obtained using CCD detectors can be extrapolated to any kind of image sensors.
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		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	CAT @ cat @ FNO2010			Serial	1285
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Author	Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte
Title	Perceptual image enhancement for smartphone real-time applications			Type	Conference Article
Year	2023	Publication	Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision	Abbreviated Journal
Volume		Issue		Pages	1848-1858
Keywords
Abstract	Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.
Address	Waikoloa; Hawai; USA; January 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	WACV
Notes	MACO; CIC			Approved	no
Call Number	Admin @ si @ CVV2023			Serial	3900
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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	CVPRW
Notes	MACO; CIC			Approved	no
Call Number	Admin @ si @ LZT2023			Serial	3910
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