Filling In Blanks

Faced with a grime-encrusted, damaged painting, a conservator can spend many months restoring the artwork. It’s often not enough to meticulously clean off dirt, remove discolored varnish, and repair torn, warped, or cracked canvas. Where paint has flaked away to expose bare spots, a conservator may need to fill in the ragged scars–a practice known as inpainting. This process is time-

consuming, highly subjective, and different for each artwork and for each professional restorer.

Aiming to make the modifications as unobtrusive as possible, a conservator uses cues from surrounding areas to guess what once adorned a painting’s missing pieces. Visible patterns and structures are then extended into the empty regions. In general, there is no single correct solution to a given problem.

Similar issues of plausible restoration arise in retouching photographs and digital images. Even with sophisticated graphics software, image inpainting remains largely a manual process. The user has to specify for the computer which areas need to be filled in and precisely what colors, forms, and textures should go into the gaps.

Researchers are now developing computer techniques to automate image inpainting. In these applications, a user simply selects the areas to be restored and a computer takes care of the rest. “We try to replicate the basic techniques used by professional restorers,” says computer engineer Guillermo Sapiro of the University of Minnesota at Minneapolis-St. Paul.

At the Joint Mathematics Meetings held in January in San Diego, Sapiro and other researchers described recent advances in automated image inpainting. Such computer techniques could significantly reduce the time and effort required to fix digital images, not only to fill in blank regions but also to remove extraneous objects–superimposed text, a distracting spectator in the background, or a political foe of the featured person–from a given scene. The process could also improve an image’s resolution or correct for losses suffered during the transmission of digital images.

Finally, this sort of software could help conservators by providing a digital canvas on which they can test various inpainting options. “It could help you decide what colors to start with,” Sapiro suggests.

Going with the flow

Inpainting has a lengthy history. Not long after the earliest paintings had been completed, someone probably had to go back to fill in areas where pigment had flaked away to reveal bare plaster, wood, or canvas. With the advent of photography, darkroom experts expanded the retouching repertoire to include techniques for filling in scratches, repairing cracks, and airbrushing away blemishes.

Nowadays, anyone with access to graphics software can readily modify digital images to remove such blights as red eye in flash photos or transport themselves from a crowded room to a pristine beach. Doing it well enough to fool even the casual eye, however, can take a great deal of time and effort.

One area where automation already plays a role is the restoration of movies. By converting a movie’s frames into a sequence of digital images, it’s possible to use a computer to detect and repair scratches and dust spots on a given frame by comparing it to adjacent frames and copying image information from intact areas.

Digital inpainting of still images is considerably more difficult and subjective because there’s usually no information available from neighboring frames or other sources. A restorer can base decisions only on whatever details are visible in the margins surrounding a blank area.

To automate image inpainting, Sapiro, Vicent Caselles and Marcelo Bertalmio of the University of Pompeu-Fabra in Barcelona and their coworkers in the past 2 years have developed algorithms that mimic the way conservators work. They extend known image characteristics, such as geometric shapes, contours, curves, lines, and color changes, from margins into blank areas. During the project, Sapiro made several visits to the Minneapolis Institute of Arts to observe how conservators restore paintings.

In their initial model, Sapiro and his team used differential equations to simulate the way pigments of various shades of gray might seep into a central pool–the hole–from the hole’s margin, or shoreline. According to how quickly the shade of gray changes at different places along the shoreline, the equations specify the directions and rates at which the shade changes throughout the pool.

Applied repeatedly, the procedure gradually fills in a given blank area, directing and mingling flows to create a stable, plausible pattern that completes the picture. For a color picture, the technique is applied independently to each of three grayscale images, which can then be combined to generate a color rendering.

“A user selects the region to inpaint,” Sapiro says. “It then takes less than a minute–maybe just a few seconds–for a [desktop computer] to run through the process.” Moreover, the method can fill in numerous regions simultaneously, even when they represent different structures against varied backgrounds.

The results aren’t always perfect, Sapiro admits. Nonetheless, even when manual procedures must be applied to correct errors, total restoration time is reduced by orders of magnitude.

Repairing defects

With plenty of room for improvement, Sapiro, Caselles, and their coworkers have studied alternative sets of equations to enhance their methods. The equations of one scheme take into account both changes in shade and the continuation of lines and shadows. This combination comes closer to matching how conservators restore a painting than does the flow model based just on shade gradients.

Flow-based techniques have trouble reproducing textures to cover large gaps. Hence, the researchers have looked into combining their approaches with standard methods for synthesizing textures. “An ideal algorithm should be able to automatically switch between textured and geometric areas and select the best-suited technique for each region,” the researchers reported in the August 2001 IEEE Transactions on Image Processing.

Andrea L. Bertozzi of Duke University in Durham, N.C., working with Sapiro and Bertalmio, has shown how the differential equations for smooth, directed flows of image intensity used by Sapiro’s team are related to the so-called Navier-Stokes equations, which are used to model the motion of air, water, and other fluids. “This opens the door to bringing computational fluid-dynamics theory and practice into computer vision and image analysis,” Bertozzi says.

Engineers and physicists have already developed a wealth of techniques for solving the Navier-Stokes equations to describe fluid flow under various conditions, whether in a wind tunnel or water tank (SN: 3/18/95, p. 168). They can now look forward to the use of these techniques in image processing. Frame-by-frame video inpainting is one possible application, Bertozzi suggests.

Differential equations and flows are also starting to play a role in novel techniques to sharpen blurry images or reduce speckling caused by transmission of digital images over noisy channels. Incorporating inpainting as part of the deblurring and denoising process is now feasible, says applied mathematician Tony F. Chan of the University of California, Los Angeles. Such approaches to image restoration hark back to methods originally developed to model shock waves and to algorithms for tracking fluid motions at interfaces (SN: 4/10/99, p. 232: https://www.sciencenews.org/sn_arc99/4_10_99/bob1.htm).

Publications struggle to obtain images with enough resolution to produce a clear, sharp picture on the page. Software to increase image resolution would have wide application, and digital inpainting techniques offer a possible solution.

A digital image can be regarded as a square grid of points, or pixels, with each point having a particular shade of gray. Suppose an image is represented by a square grid that’s 64 pixels wide. If the image’s width and height were doubled, the shade of each original pixel would fill an area four times as big, giving the enlarged image a jagged, blocky look. Algorithms based on the way fluids diffuse could be used to smooth out blocks and add detail, while still preserving the sharp lines and smooth curves that the eye perceived in the smaller image, Chan suggests.

Similar questions of restoration arise when digital images are electronically compressed and pixel information is lost during transmission. Sapiro and his colleagues have recently worked out techniques for restoring compressed images. “We have shown that as long as the features in the image are not completely lost, they can be satisfactorily reconstructed using a combination of computationally efficient image-inpainting and texture-synthesis algorithms,” the researchers report in a paper submitted for publication.

Other recent approaches to automated image inpainting stem from computer-vision research. These employ algorithms for detecting specific image features such as lines and shadows and establishing that seemingly separate pieces belong to a single structure. In that way, it’s possible to take advantage of visible image characteristics to guide inpainting. “A good image model leads to a good inpainting model,” says University of Minnesota mathematician Jianhong Shen.

Whether digital or manual, however, inpainting is always an attempt to make up for lost information. In many situations, there may be multiple solutions to how a gap can be filled in to produce a plausible result.

Ultimately, judgment resides in the eye of the beholder. “Can you tell where the image was changed?” Chan asks. “If you can’t tell, we’ve been successful.”