Photorealistic Style Transfer with Screened Poisson Equation

Recent work has shown impressive success in transferring painterly style to images. These approaches, however, fall short of photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. In this paper we propose an approach that takes as input a stylized image and makes it more photorealistic. It relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Our method is fast, simple, fully automatic and shows positive progress in making a stylized image photorealistic. Our results exhibit finer details and are less prone to artifacts than the state-of-the-art.Comment: presented in BMVC 201

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler's Elastica based approach that properly deals with random noises to improve the segmentation performance for noisy images. We solve the corresponding optimization problem via using the progressive hedging algorithm (PHA) with a step length suggested by the alternating direction method of multipliers (ADMM). Technically, all the simplified convex versions of the subproblems derived from the major framework of PHA can be obtained by using the curvature weighted approach and the convex relaxation method. Then an alternating optimization strategy is applied with the merits of using some powerful accelerating techniques including the fast Fourier transform (FFT) and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which validated some significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithm

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

Інтелектуальна система фотореалістичного перенесення стилів між зображеннями

Магістерська дисертація: 72 с., 16 рис., 23 табл., 2 додатки та 26 джерел. Об’єктом дослідження є методи технічного зору та перенесення стилів між зображеннями. Метою даної роботи є розробка системи фотореалістичного перенесення стилів між знімками, а також реалізація додатку для демонстрації роботи системи. У роботі проаналізовано методи технічного зору, проведено огляд існуючих підходів перенесення стилів та підвищення реалістичності зображень. Методом дослідження є модель, що поєднує техніки комп’ютерного зору та глибокого навчання. Результати роботи: - запропоновано алгоритм реалістичного перенесення стилю між фотознімками; - реалізовано запропоновану конфігурацію для системи перетворення похмурої погоди на фотознімках на сонячну; - створено додаток із зручним для користувача графічним інтерфейсом. Результати даної роботи рекомендується використовувати у фоторедакторах та інших сервісах для обробки зображень. При подальших дослідженнях у цій області доцільно покращити точність перенесення стилю та підвищити швидкість обробки кадрів.The master’s thesis: 72 p., 16 fig., 23 tabl., 2 appendices and 26 sources. The theme of this thesis is ―Intelligent system of photorealistic style transfer between images‖. Object of study: computer vision and style transfer techniques. The purpose of this thesis is to develop system of photorealistic style transfer between images. Several methods of computer vision and style transfer were analyzed in the thesis. The method of observation: a model that combines computer vision and deep learning techniques. The results of the thesis: - an algorithm for photorealistic style transfer between images was proposed; - the proposed configuration was implemented for transferring sunny weather to the photo; - an application with graphical user interface to demonstrate the system’s work was developed. The results of this thesis are recommended for use in the photoeditors and another applications for editing images. In further research it is reasonable to improve the faithfulness and increase the speed of image processing

Screened Poisson Equation for Image Contrast Enhancement

In this work we propose a discussion and detailed implementation of a very simple gradient domain method that tries to eliminate the effect of nonuniform illumination and at the same time preserves the images details. This model, which to the best of our knowledge has not been explored in spite of its simplicity, acts as a high pass filter. We show that with a single contrast parameter (which keeps the same value in most experiments), the model delivers state of the art results. They compare favorably to results obtained with more complex algorithms. Our algorithm is designed for all kinds of images, but with the special specification of making minimal image detail alteration thanks to a first order fidelity term, instead of the usual zero order term. Experiments on non-uniform medical images and on hazy images illustrate significant perception gain