Gotowa bibliografia na temat „Aesthetic image enhancement”
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Artykuły w czasopismach na temat "Aesthetic image enhancement"
Zhao, Xiaoyan, Ling Shi, Zhao Han i Peiyan Yuan. "A Mobile Image Aesthetics Processing System with Intelligent Scene Perception". Applied Sciences 14, nr 2 (18.01.2024): 822. http://dx.doi.org/10.3390/app14020822.
Pełny tekst źródłaZhang, Fang-Lue, Miao Wang i Shi-Min Hu. "Aesthetic Image Enhancement by Dependence-Aware Object Recomposition". IEEE Transactions on Multimedia 15, nr 7 (listopad 2013): 1480–90. http://dx.doi.org/10.1109/tmm.2013.2268051.
Pełny tekst źródłaGhose, Tandra, Yannik Schelske, Takeshi Suzuki i Andreas Dengel. "Low-level pixelated representations suffice for aesthetically pleasing contrast adjustment in photographs". Psihologija 50, nr 3 (2017): 239–70. http://dx.doi.org/10.2298/psi1703239g.
Pełny tekst źródłaZhang, Xin, Xinyu Jiang, Qing Song i Pengzhou Zhang. "A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment". Electronics 12, nr 11 (3.06.2023): 2526. http://dx.doi.org/10.3390/electronics12112526.
Pełny tekst źródłaHusselman, Tammy-Ann, Edson Filho, Luca W. Zugic, Emma Threadgold i Linden J. Ball. "Stimulus Complexity Can Enhance Art Appreciation: Phenomenological and Psychophysiological Evidence for the Pleasure-Interest Model of Aesthetic Liking". Journal of Intelligence 12, nr 4 (3.04.2024): 42. http://dx.doi.org/10.3390/jintelligence12040042.
Pełny tekst źródłaLee, Tae Sung, i Sanghoon Park. "Contouring the Mandible for Aesthetic Enhancement in Asian Patients". Facial Plastic Surgery 36, nr 05 (październik 2020): 602–12. http://dx.doi.org/10.1055/s-0040-1717080.
Pełny tekst źródłaVeinberga, Maija, Daiga Skujane i Peteris Rivza. "The impact of landscape aesthetic and ecological qualities on public preference of planting types in urban green spaces". Landscape architecture and art 14 (16.07.2019): 7–17. http://dx.doi.org/10.22616/j.landarchart.2019.14.01.
Pełny tekst źródłaGuo, Guanjun, Hanzi Wang, Chunhua Shen, Yan Yan i Hong-Yuan Mark Liao. "Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression". IEEE Transactions on Multimedia 20, nr 8 (sierpień 2018): 2073–85. http://dx.doi.org/10.1109/tmm.2018.2794262.
Pełny tekst źródłaHu, Kai, Chenghang Weng, Chaowen Shen, Tianyan Wang, Liguo Weng i Min Xia. "A multi-stage underwater image aesthetic enhancement algorithm based on a generative adversarial network". Engineering Applications of Artificial Intelligence 123 (sierpień 2023): 106196. http://dx.doi.org/10.1016/j.engappai.2023.106196.
Pełny tekst źródłaHu, Yaopeng. "Optimizing e-commerce recommendation systems through conditional image generation: Merging LoRA and cGANs for improved performance". Applied and Computational Engineering 32, nr 1 (22.01.2024): 177–84. http://dx.doi.org/10.54254/2755-2721/32/20230207.
Pełny tekst źródłaRozprawy doktorskie na temat "Aesthetic image enhancement"
Payne, Andrew. "Automatic aesthetic image enhancement for consumer digital photographs". Thesis, Loughborough University, 2007. https://dspace.lboro.ac.uk/2134/34568.
Pełny tekst źródłaGoswami, Abhishek. "Content-aware HDR tone mapping algorithms". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG013.
Pełny tekst źródłaThe ratio between the brightest and the darkest luminance intensity in High Dynamic Range (HDR) images is larger than the rendering capability of the output media. Tone mapping operators (TMOs) compress the HDR image while preserving the perceptual cues thereby modifying the subjective aesthetic quality. Age old painting and photography techniques of manual exposure correction has inspired a lot of research for TMOs. However, unlike the manual retouching process based on semantic content of the image, TMOs in literature have mostly relied upon photographic rules or adaptation principles of human vision to aim for the 'best' aesthetic quality which is ill-posed due to its subjectivity. Our work reformulates the challenges of tone mapping by stepping into the shoes of a photographer, following the photographic principles, image statistics and their local retouching recipe to achieve the tonal adjustments. In this thesis, we present two semantic aware TMOs – a traditional SemanticTMO and a deep learning-based GSemTMO. Our novel TMOs explicitly use semantic information in the tone mapping pipeline. Our novel GSemTMO is the first instance of graph convolutional networks (GCN) being used for aesthetic image enhancement. We show that graph-based learning can leverage the spatial arrangement of semantic segments like the local masks made by experts. It creates a scene understanding based on the semantic specific image statistics a predicts a dynamic local tone mapping. Comparing our results to traditional and modern deep learning-based TMOs, we show that G-SemTMO can emulate an expert’s recipe and reach closer to reference aesthetic styles than the state-of-the-art methods
Raud, Charlie. "How post-processing effects imitating camera artifacts affect the perceived realism and aesthetics of digital game graphics". Thesis, Södertörns högskola, Institutionen för naturvetenskap, miljö och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-34888.
Pełny tekst źródłaDenna studie undersöker hur post-processing effekter påverkar realismen och estetiken hos digital spelgrafik. Fyra fokusgrupper utforskade en digital spelmiljö medan olika post-processing effekter exponerades för dem. Under kvalitativa fokusgruppsintervjuer fick de frågor angående deras upplevelser och preferenser och detta resultat blev sedan analyserat. Resultatet kan ge en bild av de olika för- och nackdelarna som finns med dessa populära post-processing effekter och skulle möjligen kunna hjälpa grafiker och spelutvecklare i framtiden att använda detta verktyg (post-processing effekter) så effektivt som möjligt.
Huang, Yong-Jian, i 黃詠健. "Developing a Professional Image Enhancement Mechanism Based on Contemporary Photograph Aesthetics Criteria Mining". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/17283014763907672061.
Pełny tekst źródła國立臺灣科技大學
資訊工程系
103
In recent years, the rise of smartphones and digital cameras makes it easier to take photos and a mass amount of photos are spread on the Internet. Photographic aesthetics is some sort of art which is expressed by the professional photographers’ aesthetic sensibilities and emotion. Moreover, many professional photographers make adjustments to the photos in post, and let photos much become more beautiful and meet the conditions of photographic aesthetics rules. Enhancing the images followed by ambiguous photographic aesthetics become a big task for computer. In this thesis, an automatically image enhancement based on the aesthetics images dataset from the internet is proposed. We used many method to analyze an image such as RMS method, Laplace of Gaussian method, saliency map method, Gabor filter method and so on. We can use above sixteen features extracted from image to judge an image is good or not. We present a new concept to enhance images by using cluster styles which are generated from X-means and CART decision tree. When an input image is judged as a bad image by CART decision tree, the reason can be traced back by the decision tree characteristic to know which features needs enhancement. We list ten features which can enhance image efficiently such as gamma correction, Gaussian blur and so on. We use Interval Halving method to approach the value which come from giving suggestion of a feature by CART decision tree based on contemporary aesthetics criteria. In the experiments, we apply cluster and classification to our dataset, and the average of cluster’s accuracy is 96.8%. In the enhancement part, we use CART decision tree aesthetic suggestion which means some feature are not enough or some feature are too high that can enhance our image step by step. Then we can get differently image style result like professional photographers do.
Części książek na temat "Aesthetic image enhancement"
Chaudhary, Priyanka, Kailash Shaw i Pradeep Kumar Mallick. "A Survey on Image Enhancement Techniques Using Aesthetic Community". W Advances in Intelligent Systems and Computing, 585–96. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5520-1_53.
Pełny tekst źródłaOlsson, Liselott Mariett, Robert Lecusay i Monica Nilsson. "Children and Adults Explore Human Beings’ Place in Nature and Culture: A Swedish Case-Study of Early Childhood Commons for More Equal and Inclusive Education". W Educational Commons, 29–48. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51837-9_3.
Pełny tekst źródła"Aesthetic Enhancement". W The Texture of Images, 183–253. BRILL, 2020. http://dx.doi.org/10.1163/9789004440128_007.
Pełny tekst źródłaStreszczenia konferencji na temat "Aesthetic image enhancement"
Zavalishin, Sergey S., i Yuri S. Bekhtin. "Visually aesthetic image contrast enhancement". W 2018 7th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2018. http://dx.doi.org/10.1109/meco.2018.8406077.
Pełny tekst źródłaLi, Ling, Dong Liang, Yuanhang Gao, Sheng-Jun Huang i Songcan Chen. "ALL-E: Aesthetics-guided Low-light Image Enhancement". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/118.
Pełny tekst źródłaDu, Xiaoyu, Xun Yang, Zhiguang Qin i Jinhui Tang. "Progressive Image Enhancement under Aesthetic Guidance". W ICMR '19: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3323873.3325055.
Pełny tekst źródłaDeng, Yubin, Chen Change Loy i Xiaoou Tang. "Aesthetic-Driven Image Enhancement by Adversarial Learning". W MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240531.
Pełny tekst źródłaBakhshali, Mohamad Amin, Mousa Shamsi i Amir Golzarfar. "Facial color image enhancement for aesthetic surgery blepharoplasty". W 2012 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2012). IEEE, 2012. http://dx.doi.org/10.1109/isiea.2012.6496659.
Pełny tekst źródłaLi, Leida, Yuzhe Yang i Hancheng Zhu. "Naturalness Preserved Image Aesthetic Enhancement with Perceptual Encoder Constraint". W ICMR '19: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3323873.3326591.
Pełny tekst źródłaZhang, Xiaoyan, Martin Constable i Kap Luk Chan. "Aesthetic enhancement of landscape photographs as informed by paintings across depth layers". W 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6115622.
Pełny tekst źródłaLiu, Xiangfei, Xiushan Nie, Zhen Shen i Yilong Yin. "Joint Learning of Image Aesthetic Quality Assessment and Semantic Recognition Based on Feature Enhancement". W ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414367.
Pełny tekst źródłaChitale, Manasi, Mihika Choudhary, Ritika Jagtap, Priti Koutikkar, Lata Ragha i Chaitanya V. Mahamuni. "High-Resolution Image-to-Image Translation for Aesthetic Enhancements Using Generative Adversarial Network". W 2024 2nd International Conference on Disruptive Technologies (ICDT). IEEE, 2024. http://dx.doi.org/10.1109/icdt61202.2024.10489200.
Pełny tekst źródłaZheng, Naishan, Jie Huang, Qi Zhu, Man Zhou, Feng Zhao i Zheng-Jun Zha. "Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images". W MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3547952.
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