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Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>While self-attention has been successfully applied in a variety of natural language processing and computer vision tasks, its application in Monte Carlo (MC) image denoising has not yet been well explored. This paper presents a self-attention based MC denoising deep learning network based on the fact that self-attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for the denoising task. Particularly, we modify the standard self-attention mechanism to an auxiliary feature guided self-attention that considers the by-products (e.g., auxiliary feature buffers) of the MC rendering process. As a critical prerequisite to fully exploit the performance of self-attention, we design a multi-scale feature extraction stage, which provides a rich set of raw features for the later self-attention module. As self-attention poses a high computational complexity, we describe several ways that accelerate it. Ablation experiments validate the necessity and effectiveness of the above design choices. Comparison experiments show that the proposed self-attention based MC denoising method outperforms the current state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3478513.3480565","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:28:45Z","timestamp":1639160925000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Monte Carlo denoising via auxiliary feature guided self-attention"],"prefix":"10.1145","volume":"40","author":[{"given":"Jiaqi","family":"Yu","sequence":"first","affiliation":[{"name":"South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongwei","family":"Nie","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjiang","family":"Long","sequence":"additional","affiliation":[{"name":"JD Finance America Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenju","family":"Xu","sequence":"additional","affiliation":[{"name":"InnoPeak Technology Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiqing","family":"Li","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba , Jamie Ryan Kiros, and Geoffrey E Hinton . 2016 . 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