Image inpainting was historically done manually by painters for removing defect from paintings and photographs. Fill the region of missing information from a signal using surrounding information and re-form signal is the basic work of inpainting algorithms. Image inpainting is an art of missing value or a data in an image. The purpose of image inpainting is to reconstruct missing regions which is visible for human eyes. Image inpainting is the process of reconstructing lost part of images based on the background information.
The modification of images in a way that is non-detectable for an observer who does not know the original image is a practice as old as artistic creation itself. The need to retouch the image in an unobtrusive way extended naturally from paintings to photography and film. The purposes remain the same to revert deterioration (e.g., cracksin photographs or scratches and dust spots in film), or to add or remove elements (e.g., removal of stamped date and red-eye from photographs, the infamous “airbrushing” of political enemies). Inpainting is the art of restoring lost parts of an image and reconstructing them based on the background information. This has to be done in an undetectable way. The term inpainting is derived from the ancient art of restoring image by professional image restorers in museums etc. Digital Image Inpainting tries to imitate this process and perform the inpainting automatically.
“Inpainting is the process of reconstructing lost or deteriorated parts of images and videos. For instance, in the case of a valuable painting, this task would be carried out by a skilled image restoration artist. In the digital world, inpainting (also known as image interpolation or video interpolation) refers to the application of sophisticated algorithms to replace lost or corrupted parts of the image data (mainly small regions or to remove little defects”.
Figure 1 show an example of this technique where a building (manually selected as the target region) is replaced by information from the remaining of the image in a visually plausible way. The algorithm automatically does this in a way that it looks “reasonable” to the human eye. Details that are hidden/ occluded completely by the object to be removed cannot be recovered by any mathematical method. Therefore the objective for image inpainting is not to recover the original image, but to create some image that has a close resemblance with the original image.
Figure 1 Removing objects using Image Inpainting
In this figure, (a) The original image, (b) Image with the building removed. Notice how the contour of mountain and the textures have both been corrected.
Another use of image inpainting is in creating special effects by removing unwanted objects from the image. Unwanted objects may range from microphones, ropes, some unwanted person and logos, stamped dates and text etc. in the image. During the transmission of images over a network, there may be some parts of an image that are missing. These parts can then be reconstructed using image inpainting. There have also been a few researches on how to use image inpainting for super-resolution and zooming of images.
Image Inpainting Techniques
Image inpainting algorithms can be classified into different categories like texture synthesis based image inpainting, Exemplar and search based image inpainting, PDE (Partial Differential Equation) based inpainting, Fast semiautomatic inpainting and hybrid inpainting. Here in this section we have explained all of these inpainting methods.
- Partial Differential Equation (PDE) based: The algorithm is to continue geometric and photometric information that arrives at the border of the occluded area into area itself. This is done by propagating the information in the direction of minimal change using is ophotelines. This algorithm will produce good results if missed regions are small one
- Texturesynthesis based: The Texture synthesis is a field of study independent from, but related to inpainting. In the general definition of this problem, an input sample of a texture is given, and the goal is to produce more of that texture. The simplest solution is to tile the texture sample on a rectangular grid of desired size.
- Exemplar and search based: The exemplar based consists of two basic steps 1.priority assignment is done and the 2.the selection of the best matching patch. The exemplar based approach samples the best matching patches from the known region, whose similarity is measured by certain metrics, and pastes into the target patches in the missing region. Exemplar- based Inpainting iteratively synthesizes the unknown region i. e. target region, by the most similar patch in the source region
- Wavelet Transform based: We expect the best global structure estimation of damaged regions in addition to shape and texture properties. If we consider the fact of multi-resolution analysis, data separation, compaction along with the statistical properties then we have to consider the wavelet transform due to its good image representation quality. Wavelet transform try to satisfy the human visual system (HVS)
- Semi-automatic and Fast Inpainting: This image in painting requires user assistance the in the form of guide lines to help in structure completion has found favor with researchers. This technique results in blur effect in image.
 Pritika Patel and Pritika Patel, “Review of Different Inpainting Algorithms”, International Journal of Computer Applications (IJCA) Volume 59– No.18, December 2012
 Rajul Suthar and Mr. Krunal R. Patel, “A Survey on Various Image Inpainting Techniques to Restore Image”, International Journal of Engineering Research and Applications, Vol. 4, Issue 2( Version 1), February 2014, pp.85-88
 Nirali Pandya and Bhailal Limbasiya, “A Survey on Image Inpainting Techniques”, International Journal of Current Engineering and Technology, 2013.