Color image processing problems in digital photography

In this thesis, we discuss three image processing topics: High Dynamic Range (HDR) image creation in scenes with motion, Tone Mapping (TM), and Demosaicking. The first part of this thesis focuses on the creation of HDR images using gradient fusion techniques, and proposes a method that deals with motion and avoids bleeding and ghost artifacts. In the second part, we tackle the TM problem, whose goal is to produce a low dynamic range picture from an HDR image that reproduces the sensation of an observer in the scene. We review the perceptual principles that we find important for TM purposes and present a new method that compares well to the state of the art. Finally, we propose a new method to reconstruct the three color channels of a picture taken with a Bayer filter. This problem is called Demosaicking and will be presented in the third part of this thesis.En esta tesis tratamos tres temas de procesamiento de imagen: creación de imágenes de alto rango dinámico o HDR, Tone Mapping (TM) y Demosaicking. En la primera parte proponemos un método para la creación de imágenes HDR con movimiento que permite generar resultados sin artefactos de tipo bleeding y ghosting. En la segunda parte de la tesis tratamos el problema de TM cuyo objetivo es comprimir el rango dinámico de una imagen HDR para ser mostrada en una pantalla o impresa, simulando lo mejor posible la percepción de un sujeto en la escena. Presentaremos los principios sicofísicos que consideramos relevantes para TM y propondremos un método nuevo que mejora los resultados del estado del arte. Finalmente, en la tercera parte presentamos un método de Demosaicking o reconstrucción de los tres canales de color de una imagen tomada con un filtro de Bayer

Adaptive Color Transfer With Relaxed Optimal Transport

5 pagesInternational audienceThis paper tackles the problem of color transfer between images using discrete optimal transportation. Methods that only match discrete image color palettes through bijective assignments [1], without considering the spatial pixel location such as [2,3], suffer from several limitations such as spatial inconsistencies or noise amplification, so that additional post-processing must be considered in practice [1,4]. To tackle these issues, we here propose a variational model that directly deals with the regularity of the transport map and the spatial consistency of the reconstruction. Our approach is based on the relaxed and regularized discrete optimal transport method of [5]. We extend this model in order to take into account the spatial distribution of colors within the image domain and only relax locally the bijective assignment constraint, when required by the data. We rely on a spatio-color clustering framework to ensure fast computation while preserving the quality of the color transfer. Finally, we present some experiments on real images that demonstrate the capacity of our model to adapt itself to the considered data. [1] Piti e, F., Kokaram, A.C., Dahyot, R.: Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding 107 (2007) 123-137 [2] Papadakis, N., Provenzi, E., Caselles, V.: A variational model for histogram transfer of color images. IEEE Transactions on Image Processing 20 (2011) 1682-1695 [3] Rabin, J., Peyr e, G.: Wasserstein regularization of imaging problem. In: IEEE International Conderence on Image Processing (ICIP'11). (2011) 1541-1544 [4] Rabin, J., Delon, J. and Gousseau, Y.: Regularization of transportation maps for color and contrast transfer, In: IEEE International Conderence on Image Processing (ICIP'10). (2010) 1933-1936 [5] Ferradans, A., Papadakis, N., Rabin, J., Peyré, G. and Aujol, J-F. Regularized discrere optimal transport. In: International Conference on Scale Space and Variational Methods in Computer Vision (2013) 1-1

Adaptive Color Transfer With Relaxed Optimal Transport

This paper tackles the problem of color transfer between images using discrete optimal transportation. Methods that only match discrete image color palettes through bijective assignments [1], without considering the spatial pixel location such as [2,3], suffer from several limitations such as spatial inconsistencies or noise amplification, so that additional post-processing must be considered in practice [1,4]. To tackle these issues, we here propose a variational model that directly deals with the regularity of the transport map and the spatial consistency of the reconstruction. Our approach is based on the relaxed and regularized discrete optimal transport method of [5]. We extend this model in order to take into account the spatial distribution of colors within the image domain and only relax locally the bijective assignment constraint, when required by the data. We rely on a spatio-color clustering framework to ensure fast computation while preserving the quality of the color transfer. Finally, we present some experiments on real images that demonstrate the capacity of our model to adapt itself to the considered data. [1] Piti e, F., Kokaram, A.C., Dahyot, R.: Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding 107 (2007) 123-137 [2] Papadakis, N., Provenzi, E., Caselles, V.: A variational model for histogram transfer of color images. IEEE Transactions on Image Processing 20 (2011) 1682-1695 [3] Rabin, J., Peyr e, G.: Wasserstein regularization of imaging problem. In: IEEE International Conderence on Image Processing (ICIP'11). (2011) 1541-1544 [4] Rabin, J., Delon, J. and Gousseau, Y.: Regularization of transportation maps for color and contrast transfer, In: IEEE International Conderence on Image Processing (ICIP'10). (2010) 1933-1936 [5] Ferradans, A., Papadakis, N., Rabin, J., Peyré, G. and Aujol, J-F. Regularized discrere optimal transport. In: International Conference on Scale Space and Variational Methods in Computer Vision (2013) 1-12Transport Optimal et Modèles Multiphysiques de l'ImageSparsity, Image and Geometry to Model Adaptively Visual Processing

An Algorithmic Analysis of Variational Models for Perceptual Local Contrast Enhancement

Color cast cancellation and local contrast enhancement are very important problems in computervision. In this paper we review the algorithm proposed by Palma-Amestoy et al. [A perceptuallyinspired variational framework for color enhancement, IEEE Transactions on Pattern Analysisand Machine Intelligence, 21 (2009), pp. 458–474], present results and evaluate the impact of achange in the parameters

Geometry-based demosaicking

Demosaicking is a particular case of interpolation problems where, from a scalar image in which each pixel has either the red, the green or the blue component, we want to interpolate the full-color image. State-of-the-art demosaicking algorithms perform interpolation along edges, but these edges are estimated locally. We propose a level-set-based geometric method to estimate image edges, inspired by the image in-painting literature. This method has a time complexity of O(S) , where S is the number of pixels in the image, and compares favorably with the state-of-the-art algorithms both visually and in most relevant image quality measures