]] Inpainting is a Art conservation process where damaged, deteriorated, or missing parts of an artwork are filled in to present a complete image.
With its roots in physical artwork, such as painting and sculpture, traditional inpainting is performed by a trained art conservator who has carefully studied the artwork to determine the mediums and techniques used in the piece, potential risks of treatments, and ethical appropriateness of treatment.
It was during the 1930 International Conference for the Study of Scientific Methods for the Examination and Preservation of Works of Art, that the modern approach to inpainting was established. Helmut Ruhemann (1891–1973), a German restorer and conservator, led the discussions on the use of inpainting in conservation. Helmut Ruhemann was a leading figure in modernizing restoration and conservation. His greatest contribution to the field of conservation "was his insistence on following the methods of the original painter exactly, and on understanding the painter's artistic intention". After his career of over 40 years as a conservator, Ruhemann published his treatise The Cleaning of Paintings: Problems & Potentialities in 1968. In describing his method, Ruhemann states that "The surface of should be slightly lower than that of the surrounding paint to allow for the thickness of the inpainting...Inpainting medium should look and behave like the original medium, but must not darken with age."Garland, Patricia, 2011, Chapter 3, Tradition of retouching Practices in America
Retrieved November 2, 2019. Cesare Brandi (1906–1988) developed the teoria del restauro, the inpainting approach combining aesthetics and psychology. However, this approach was used primarily by Italian restorers and conservators, with the terminology becoming widespread in the 1990s.
Technological advancements led to new applications of inpainting. Widespread use of digital techniques range from entirely automatic computerized inpainting to tools used to simulate the process manually. Since the mid-1990s, the process of inpainting has evolved to include digital media. More commonly known as image or video interpolation, a form of estimation, digital inpainting includes the use of Software that relies on sophisticated to replace lost or corrupted parts of the image data.
There are several ethic considerations before Inpainting can be justified. Various deliberation decisions over the ethical appropriateness of the amount and type of inpainting done, resides on many factors. As most conservation treatments, inpainting's ethical questions rest mainly with authenticity, reversibility and documentation.
Any intervention to compensate for loss should be documented in treatment records and reports and should be detectable by common examination methods. Such compensation should be reversible and should not falsely modify the known aesthetic, conceptual, and physical characteristics of the cultural property, especially by removing or obscuring original material.American Institute of Conservation of Historical and Artistic Works. (1994). "AIC Code of Ethics and Guidelines for Practice". [1]. Retrieved March 27, 2020.New technologies and the aesthetic demand for perfect images without imperfections challenge conservators' ethical practices to protect the integrity of originals.Antonio laccarino Idelson. (28 June 2018). "Inpainting"
Helmut Ruhemann's inpainting techniques by Jessell have procedures to "preserve" the quality of oil and Tempera.Bettina Jessell. (1977). "Helmut Ruhemann's Inpainting Techniques". Journal of the American Institute for Conservation, JAIC 1977, Vol. 17, Number 1, Article 1 (pp. 08 - 08). Retrieved March 26, 2020.
In special effect, inpainting is usually performed after video matting. They can also be observed in applications like image compression and super-resolution. In photography and Film, it is used for film restoration to reverse, repair, or mitigate deterioration (e.g., physical damage such as cracks in photographs, scratches and dust spots in film, or chemical damage resulting in image loss; performed infrared cleaning). It can also be used for removing red-eye, the stamped date from photographs, and objects for creative effect. This technique can be used to replace any lost blocks in the coding and transmission of images, for example, in a streaming video. It can also be used to remove logos or watermarks in videos.
Deep learning neural network-based inpainting can be used for decensoring images. Deep image prior-based techniques can be used for digital image inpainting, where a trained deep learning model is either unavailable or infeasible.
Three main groups of 2D image-inpainting algorithms can be found in the literature. The first one to be noted is structural (or geometric) inpainting, the second one is texture inpainting, the last one is a combination of these two techniques. They use the information of the known or non-destroyed image areas in order to fill the gap, similar to how physical images are restored.
A more traditional method is to use differential equations (such as Laplace's equation) with Dirichlet boundary conditions for continuity so as to create a seemingly seamless fit. This works well if missing information lies within the homogeneous portion of an object area.
Other methods follow Contour line directions (in an image, a contour of equal luminance), to do the inpainting.
Model based inpainting follows the Bayesian approach for which missing information is best fitted or estimated from the combination of the models of the underlying images, as well as the image data actually being observed. In deterministic language, this has led to various variational inpainting models.
Manual computer methods include using a clone tool to copy existing parts of the image to restore a damaged texture. Texture synthesis may also be used.
Exemplar-based image inpainting attempts to automate the clone tool process. It fills "holes" in the image by searching for similar patches in a nearby source region of the image, and copying the pixels from the most similar patch into the hole. By performing the fill at the patch level as opposed to the pixel level, the algorithm reduces blurring artifacts caused by prior techniques.
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