Efficient Poisson Image Editing

Image composition refers to the process of composing two or more images to create a natural output image. It is one of the important techniques in image processing. In this paper, two efficient methods for composing color images are proposed. In the proposed methods, the Poisson equation is solved using image pyramid and divide-and-conquer methods. The proposed methods are more efficient than other existing image composition methods. They reduce the time taken in the composition process while achieving almost identical results using the previous image composition methods. In the proposed methods, the Poisson equation is solved after converting it to a linear system using different methods. The results show that the time for composing color images is decreased using the proposed methods

Efficient Poisson Image Editing

Image composition refers to the process of composing two or more images to create an acceptable output image. It is one of the important techniques of image processing. In this paper, two efficient methods for composing color images are proposed. In the proposed methods, the Poisson equation is solved using image pyramid, and divide-and-conquer methods. The proposed methods are more efficient than other existing image composition methods. They reduce the time taken in the composition process while achieving almost identical results using the previous image composition methods. In the proposed methods, the Poisson equation is solved after converting it to a linear system using different methods. The results show that the time for composing color images is decreased using the proposed methods

Implementació de l'algorisme Poisson image editing a la plataforma Android

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2015, Director: Lluís Garrido OstermannThis project’s aim is to develop an Android application that implements a Poisson image editing technique, more specifically, the seamless cloning part. The implemented algorithm’s goal is to paste an area of an image onto another trying to disguise the patch inserted and making it believable to the human eye that the patch belongs to the target image. In order to make this possible some local changes have to be made through the patch to minimize the slope between the two images but simultaneously not losing any information contained in the patch. Such information is the gradient, and it is preserved through all the process. Thus, we call this process a gradient-guided interpolation. This algorithm is defined on the paper Poisson Image Editing written by Perez et Al. written in 2003. Along this project we will see similar image processing techniques and discuss the pros and cons of their usage. Afterwards, we will focus on the Poisson equations for this problem and the several ways to solve it. We will be using a Gauss-Seidel iterative solver with successive overelaxation. Then, the design and implementation of all the Android application will be discussed. Finally the results obtained by developing this project will be evaluated, ranging from the efficiency or the effectiveness of the algorithm to the plausibility of this program as a published app

Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning

Deep learning has shown great success in high-level image analysis problems; yet its efficacy relies on the quality and diversity of the training data. In this work, we introduce a copypaste image augmentation for ultrasound images. The Poisson image editing technique is used to generate realistic and seamless boundary transitions around the pasted image. Results showed that the proposed image augmentation technique improves training performance in terms of higher objective metrics and more stable training results

FaceShop: Deep Sketch-based Face Image Editing

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.Comment: 13 pages, 20 figure

Improved revealing of hidden structures and defects for historic art sculptures using poisson image editing

[EN] Radiography is a non-destructive tool and offers the acquisition of detailed information on the internal features of sculptures as a cultural heritage. However, radiographs contain different levels of blurriness mainly caused by the detection of scattered X-rays. Reduction of image blurriness provides improved contrast in targeted areas which enhances the extraction of information from the selected regions and features of the radiographs. In this study, we applied a set of convolution methods to a group of radiographic images of historic sculptures. Radiographs of the objects were provided with the associated documentation from the collection of the Radiographic Inspection Laboratory of the Universitat Politecnica de Valencia. The selection of the particular objects was based on the difference in the materials used in their construction i.e. the objects were made of wood, paper, or wax. The Poisson Image Editing (PIE) based on L-2-norm was applied for image enhancement of digital radiography images. The results showed that the PIE method was effective in selective region enhancement of the radiographic image contrast enabling better visualization of the objects' internal structures. The application of the implemented algorithm enabled the conservators and radiographers involved in the study to improve the visualization of the sculptures' internal features and defects enhance the defects' evaluation.Madrid García, JA.; Yahaghi, E.; Mirzapour, M.; Movafeghi, A. (2022). Improved revealing of hidden structures and defects for historic art sculptures using poisson image editing. Journal of Cultural Heritage. 55:381-390. https://doi.org/10.1016/j.culher.2022.04.0023813905

Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical descriptions of these functions are extremely concise, but their implementation in real code is tedious, especially when optimized for real-time performance on modern GPUs in interactive applications. In this work, we propose a new language, Opt (available under http://optlang.org), for writing these objective functions over image- or graph-structured unknowns concisely and at a high level. Our compiler automatically transforms these specifications into state-of-the-art GPU solvers based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate different variations of the solver, so users can easily explore tradeoffs in numerical precision, matrix-free methods, and solver approaches. In our results, we implement a variety of real-world graphics and vision applications. Their energy functions are expressible in tens of lines of code, and produce highly-optimized GPU solver implementations. These solver have performance competitive with the best published hand-tuned, application-specific GPU solvers, and orders of magnitude beyond a general-purpose auto-generated solver

Poisson image blending by 4-EDGAOR iteration via rotated five-point Laplacian operator

Poisson image blending is also known as Poisson image editing, is one of the core operation in image processing. The primary aim of this paper is to solve the Poisson image blending problem with the least number of iterations and computational time while obtaining the output images with satisfactory visual effect. In order to achieve this objective, 4-Explicit Decoupled Group Accelerated Over Relaxation (4-EDGAOR) iterative method via rotated Laplacian operator is proposed in this paper. The effectiveness of 4-EDGAOR iterative method in solving Poisson image blending problem is confirmed based on the numerical results obtained from the test examples