Inpainting in Image Analysis

Winter Term 2022

Seminar: Inpainting in Image Analysis

Kristina Schaefer, Prof. Joachim Weickert

Winter Term 2022/23

Seminar (2 h)

Object removal. (Authors: Criminisi, Pérez, Toyama)


November 3, 2022:
The topic assignment is finished. You can find your topic here or in Teams.

October 6, 2022:
Since the university wishes for online teaching for two weeks within the presentation phase of this seminar, we decided that the whole seminar will take place in an online setting.

August 5, 2022:
The website is online.

Important DatesDescriptionRegistrationRequirementsIntroductory MeetingOverview of Topics

Introductory meeting (mandatory):
The introductory meeting will take place on Nov. 3 2022 at 4-6 p.m. in a Teams meeting.
Attendance is mandatory for all participants. Do not forget to register first (see below).

Regular meetings online during the winter term 2022/23:
Thu 4-6 p.m. starting on 8th December 2022 online via Teams.

LSF registration deadline:

Write-up submission deadline:

Contents: Inpainting has been introduced as a technique to restore missing or deteriorated regions by using information from within the image. There are various ways to fill these missing parts such as simple interpolation, diffusion-based methods, or by comparing image patches. Nowadays, specialised inpainting methods can remove objects seamlessly or can even reconstruct an image using only a tiny fraction of the original information. In this seminar, we firstly cover basic concepts and move on to forefront research in the field of inpainting.

Prerequisites: The seminar is for advanced bachelor or master students in Visual Computing, Mathematics, or Computer Science. Basic mathematical knowledge (e.g. Mathematik für Informatiker I-III) and some knowledge in image processing and computer vision is required.

Language: All papers are written in English, and English is the language of presentation.

You must register for this course via the Seminar System

from September 26, 2022
until October 26, 2022

Regular attendance: You must attend all virtual seminar meetings. If you are sick, please send a medical certificate via mail to Kristina Schaefer. If you have technical difficulties, let us know as soon as possible.

Talk: Talk duration is 30 min, plus 15 min for discussion. Please do not deviate from this time schedule. It will be performed in a virtual setting. More details and a best practice guide will be made available after the introductory meeting.

Write-up: The write-up has to be handed in three weeks after the lecture period ends. The deadline is 10.03.2023. The write-up should summarise your talk and has to consist of 5 pages per speaker. Please adhere to the guidelines for write-ups posted in our Teams group. Submit your write-up in pdf format directly in the corresponding assignment in Teams.

Plagiarism: Adhere to the standards of scientific referencing and avoid plagiarism: Quotations and copied material (such as images) must be clearly marked as such, and a bibliography is required. Otherwise the seminar counts as failed. See the write-up guidelines for a detailed explanation on how to cite correctly.

Mandatory consultation: Talk preparation has to be presented to your seminar supervisor no later than one week before the talk is given. It is your responsibility to approach us timely and make your appointment for a video call.

No-shows: No-shows are unfair to your fellow students: Some talks are based on previous talks, and your seminar place might have prevented the participation of another student. Thus, in case you do not appear to your scheduled talk (except for reasons beyond your control), we reserve the right to exclude you from future seminars of our group.

Participation in discussions: The discussions after the presentations are a vital part of this seminar. This means that the audience (i.e. all paricipants) poses questions and tries to find positive and negative aspects of the proposed idea.

Being on time: All participants have to be in the seminar meeting on time. Participants that turn out to be regularly late must expect a negative influence on their grade.

We will discuss the following papers. If your registration was successful, the password will be sent to you before the first meeting.

No.   Date   Speaker Topic
1 08.12.2022
M. Bertalmio, G. Sapiro, V. Caselles and C. Ballester:
Image inpainting
2 08.12.2022
A. Criminisi, P. Pérez, and K. Toyama:
Region Filling and Object Removal by Exemplar-Based Image Inpainting
3 15.12.2022
D. Ding, S. Ram, and J. J. Rodriguez:
Perceptually Aware Image Inpainting
4 15.12.2022
G. Facciolo, P. Arias, V. Caselles, and G. Sapiro:
Exemplar-Based Interpolation of Sparsely Sampled Images
5 22.12.2022
C. Barnes, E. Shechtman, A. Finkelstein and D.B. Goldman:
PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing
6 22.12.2022
P. Getreuer:
Total Variation Inpainting using Split Bregman
7 05.01.2023
N. Kämper and J. Weickert:
Domain decomposition algorithms for real-time homogeneous diffusion inpainting in 4K.
8 05.01.2023
C. Schmaltz, P. Peter, M. Mainberger, F. Ebel, J. Weickert, and A. Bruhn:
Understanding, Optimising, and Extending Data Compression with Anisotropic Diffusion
9 12.01.2023
Sarah Andris, Pascal Peter, Rahul Mohideen Kaja Mohideen, Joachim Weickert and Sebastian Hoffmann:
Inpainting-Based Video Compression in FullHD
10 12.01.2023
A. Adler, V. Emiya, M. Jafari, M. Elad, R. Gribonval, M. D. Plumbley:
Audio Inpainting
11 19.01.2023
P. Peter, J. Contelly, and J. Weickert:
Compressing Audio Signals with Inpainting-based Sparsification
12 19.01.2023
J. Xie, L. Xu and E. Chen:
Image Denoising and Inpainting with Deep Neural Networks
13 26.01.2023
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell and A. Efros:
Context Encoders: Feature Learning by Inpainting
14 26.01.2023
J. Yu, J, Yang, X. Shen, X. Lu and T. Huang:
Generative Image Inpainting with Contextual Attention
15 02.02.2023
K. Schrader and J. Weickert:
CNN-based Euler’s Elastica Inpainting with Deep Energy and Deep Image Prior
16 02.02.2023
P. Peter, K. Schrader, T. Alt and J. Weickert:
Deep Spatial and Tonal Data Optimisation for Homogeneous Diffusion Inpainting.

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