SOURCE-LINKED INTELLIGENCE

new eXplainable models that allow the user to Interact with them to benefit Digital Heritage Image Restoration

CORDIS · observation · Publication date unknown

tion and color image enhancement of old photographs and videos. Historically, image enhancement methods were rooted in tailor-made priors using well-understood physics and/or statistical models. Now, deep learning approaches leverage large amounts of data to train generative models that can hallucinate on the generated images. However, the useful versatility of deep learning approaches faces two main problems: (a) Deep models are black boxes whose inner behaviors are difficult to interpret, which is an important drawback when assessing their reliability, studying failure cases, and improving their robustness. This hinders their direct adoption in the digital heritage restoration process. Thus, explainability is a highly desirable characteristic for image enhancement models. (b) Image enhancement problems are ill-conditioned, especially for digital heritage photos (e.g., there are many plausible colorizations of a grayscale image). Yet, users rarely have a say in the process of enhancement with deep models, which is typically decided by the model based on statistical decisions. Thus

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recordType

award

status

SIGNED

region

EU

value

165312.96

unit

EUR