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Employing control variates is a classical and well-established technique for variance reduction in MC integration, typically relying on the assumption that the expectations of control variates are readily obtainable. When this condition is met, control variate theory offers a principled framework for optimizing their use by adjusting coefficients that determine the relative contribution of each control variate. However, our image-space approach introduces a technical challenge, as the expectations of the pixel-based control variates are unknown and must be estimated from additional MC samples, which are unbiased but inherently noisy. In this paper, we propose a control variate estimator designed to optimally leverage such imperfect control variates by relaxing the traditional requirement that their expectations are known. We demonstrate that our approach, which estimates the optimal coefficients while explicitly accounting for uncertainty in the expectation estimates, effectively reduces the variance of MC rendering across various test scenes.<\/jats:p>","DOI":"10.1145\/3763335","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T17:15:39Z","timestamp":1764868539000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Imperfect Image-Space Control Variates for Monte Carlo Rendering"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3980-7699","authenticated-orcid":false,"given":"Chanu","family":"Yang","sequence":"first","affiliation":[{"name":"Gwangju Institute of Science and Technology, Gwangju, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3142-0115","authenticated-orcid":false,"given":"Bochang","family":"Moon","sequence":"additional","affiliation":[{"name":"Gwangju Institute of Science and Technology, Gwangju, Republic of Korea"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Attila T. \u00c1fra. 2025. 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