{"id":21069,"date":"2024-06-08T17:49:48","date_gmt":"2024-06-08T15:49:48","guid":{"rendered":"https:\/\/saarland-informatics-campus.de\/?page_id=21069"},"modified":"2024-09-14T15:12:22","modified_gmt":"2024-09-14T13:12:22","slug":"vc-example-images","status":"publish","type":"page","link":"https:\/\/saarland-informatics-campus.de\/en\/vc-example-images\/","title":{"rendered":"Visual Computing Example Images"},"content":{"rendered":"

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Image Inpainting<\/h2>\n

The ability of image inpainting to fill in missing image data is useful for many applications. This includes restoration of damaged photographs or image editing (e.g. Photoshop, GIMP, \u2026). In this example, the campus of Saarland University has been masked and is replaced by a lake, fusing the remaining woods convincingly with the new image content.<\/p>\n

Our example relies on stable diffusion, which is mostly known as a representative for so-called text-to-image models. These neural networks take a text prompt as an input and output an image. Their reconstruction relies on a model obtained from large amounts of training data and an additional source of randomness.<\/p>\n

For inpainting, these models can be multimodal, i.e. a masked input image and a text prompt can be combined. In this case, the positive prompt was \u201ca beautiful lake in the woods\u201d combined with the negative prompt \u201cbuildings\u201d.
\nInterested readers can even try this themselves online, linkable web implementations exist, e.g.: SDXL Inpainting<\/a><\/p>\n

Relevant paper<\/a>[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_3,1_3,1_3″ module_class=”image-row” _builder_version=”4.24.3″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][et_pb_column_inner type=”1_3″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” custom_margin=”0px||0px||true|false” custom_padding=”||||true” global_colors_info=”{}”]<\/p>\n

Original Image<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/original.png” title_text=”original” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_3″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” custom_margin=”0px||0px||true|false” custom_padding=”||||true” global_colors_info=”{}”]<\/p>\n

Masked Image<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/masked.png” title_text=”masked” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_3″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” custom_margin=”0px||0px||true|false” custom_padding=”||||true” global_colors_info=”{}”]<\/p>\n

Inpainted Image<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/inpainted.png” title_text=”inpainted” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner admin_label=”Sparse Image Row” module_id=”inp2″ _builder_version=”4.24.3″ _module_preset=”default” custom_padding=”||||false|false” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Inpainting from Sparse Data<\/h2>\n

A deep learning approach has selected 15% of the original pixels from an image showing the campus of Saarland University. Inpainting allows a convincing reconstruction from this very sparse image representation. The reconstruction does not require a neural network, but relies on a filter inspired by physics: It relies on partial differential equations that describe e.g. heat transfer or concentration equilibration.<\/p>\n

In combination with data selection strategies as shown in this example, inpainting can be used for image compression<\/a> by storing only the selected data. Similar techniques are also used for adaptive sampling, e. g. to improve destructive image acquisition methods like electron microscopy.<\/p>\n

These examples have been provided by Karl Schrader (Saarland University, Mathematical Image Analysis Group).<\/p>\n

Relevant Publications:<\/strong>
Deep spatial and tonal data optimisation for homogeneous diffusion inpainting: Visit site<\/a>
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting: Visit site<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_3,1_3,1_3″ module_class=”image-row” _builder_version=”4.24.3″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][et_pb_column_inner type=”1_3″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” custom_margin=”0px||0px||true|false” custom_padding=”||||true” global_colors_info=”{}”]<\/p>\n

Original Image<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/1_original.png” title_text=”original” _builder_version=”4.24.3″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_3″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.0″ _module_preset=”default” custom_margin=”0px||0px||true|false” custom_padding=”||||true” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Sparse Image Representation<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/1_masked.png” title_text=”masked” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_3″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.0″ _module_preset=”default” custom_margin=”0px||0px||true|false” custom_padding=”||||true” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Reconstructed Image<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/1_inp-1.png” title_text=”inpainted” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner admin_label=”DragGAN Row” module_id=”dragGAN” _builder_version=”4.24.3″ _module_preset=”default” custom_padding=”||||false|false” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

DragGAN<\/h2>\n

This new AI model uses \u201cGenerative Adversarial Networks\u201d or GANs and extends possibilities of digital image processing.
Read more<\/a><\/span><\/p>\n

[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_2,1_2″ module_class=”image-row” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/DragGAN.jpg” title_text=”DragGAN” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/DragGAN2-1.jpg” title_text=”DragGAN2″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner admin_label=”Lightstage Row” _builder_version=”4.24.3″ _module_preset=”default” custom_padding=”||||false|false” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” custom_padding=”||4px|||” global_colors_info=”{}”]<\/p>\n

Lightstage<\/h2>\n

The Lightstage is a device used to generate and record various lighting conditions and light scenes in a controlled manner. The Saarbr\u00fccken Lightstage has a diameter of 3.5 meters, includes around 13,000 LEDs, all of which can be controlled individually, and also boasts 40 high-resolution 6k cameras in cinema quality. The Max Planck researchers will use the technology to investigate fundamental problems in the reconstruction and simulation of photorealistic digital characters and environments.<\/p>\n

Head of lab Dr. Marc Habermann (l.) und Prof. Christian Theobalt inside Lightstage. The Lightstage is a device used to generate and record various lighting conditions and light scenes in a controlled manner.<\/span><\/p>\n

Read more<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_2,1_2″ module_class=”image-row” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/MPI_Lightstage_64562-scaled.jpg” alt=”Laboratory manager Dr. Marc Habermann (left) and Prof. Christian Theobalt stand in the Lightstage.” title_text=”Lightstage” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/MPI_Lightstage__web.jpg” title_text=”Lightstage” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner admin_label=”SAUCE Project Row” _builder_version=”4.24.3″ _module_preset=”default” custom_padding=”||||false|false” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

SAUCE Project<\/h2>\n

Image 1: Lightfield-Camera to capture large scenes (several meter in all directions).
Image 2: <\/span>Setup in one of the TV studios of Saarl\u00e4ndischer Rundfunk
Image 3: <\/span>Cellist Isabel Gehweiler performs a polyphonic piece of music composed by herself.<\/span><\/p>\n

Video:\u00a0“Unfolding”<\/strong>, a demo of lightfield capture and processing<\/span><\/p>\n

Homepage<\/a><\/span><\/p>\n

[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_video src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/SAUCE_LF_Shoot_10_18.mp4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_video][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_2,1_2″ module_class=”image-row” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/DSC3498-scaled.jpg” title_text=”SAUCE Lightfield Camera” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/4I7A4471.jpg” title_text=”SAUCE Studio” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/4I7A4549.jpg” title_text=”SAUCE Cellist” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner admin_label=”Caption Gen Row” _builder_version=”4.24.3″ _module_preset=”default” custom_padding=”||||false|false” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Image Captioning<\/h2>\n

External language models can help for better image captions.\u00a0
<\/span>Source: Fusion Models for Improved Image Captioning<\/a><\/span><\/p>\n

[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_2,1_2″ module_class=”image-row” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/prompt_gen_image.png” alt=”Laboratory manager Dr. Marc Habermann (left) and Prof. Christian Theobalt stand in the Lightstage.” title_text=”prompt_gen_image” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”3_4″ _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/saarland-informatics-campus.de\/wp-content\/uploads\/prompt_gen_text.png” title_text=”prompt_gen_text” _builder_version=”4.24.3″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

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    \n\t\t\t\t <\/ul>\n\t\t\t\t <\/div>\n\t\t\t\t <\/div>\n\t\t\t\t <\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>Image Inpainting The ability of image inpainting to fill in missing image data is useful for many applications. This includes restoration of damaged photographs or image editing (e.g. Photoshop, GIMP, …). In this example, the campus of Saarland University has been masked and is replaced by a lake, fusing […]<\/p>\n","protected":false},"author":33,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-21069","page","type-page","status-publish","hentry"],"yoast_head":"\nVisual Computing Example Images - Saarland Informatics Campus<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/saarland-informatics-campus.de\/en\/vc-example-images\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Visual Computing Example Images - Saarland Informatics Campus\" \/>\n<meta property=\"og:description\" content=\"Image Inpainting The ability of image inpainting to fill in missing image data is useful for many applications. This includes restoration of damaged photographs or image editing (e.g. Photoshop, GIMP, …). 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