We introduce a novel workflow that will hopefully open new directions of processing and improvement in image reproduction. Existing gamut mapping algorithms can be classified into two basic categories: image-independent algorithms and image-dependent algorithms. The latter algorithms produce better reproduction; however, because they are time consuming and mathematically complex, the image-independent approach is commonly used in most imaging workflows. We suggest a new workflow that attempts to approach the image-dependent mapping method without incurring significant computational drawbacks nor requiring changes in the imaging industrial standards. The proposed method attempts to choose an appropriate gamut mapping per image without reconstructing the image gamut itself and without constructing an image-specific mapping on the fly, as required by image-dependent gamut mapping methods. Specifically, image characteristics are exploited for selection of a source gamut and a gamut mapping most appropriate for a given input image from a set of available mappings. Accordingly the proposed method is named image-guided gamut mapping. We show the practicability and advantages of the suggested workflow in several specific cases. We show that better image quality is achieved for 87% of the tested images when using the suggested workflow.
In an earlier study a Semantic Content Based Image Retrieval system was developed. The system requires a Visual Object Process Diagram - VOPD to be created for each image in the database and for the query. This is a major drawback since it requires the user and database manager to be acquainted with the rules and structures of the VOPD. This is not trivial and in fact troublesome to the naive user. To overcome this drawback two approaches are presented in this work, to provide an interface to the Image Retrieval system and to bypass the need of manually creating VOPD representations.
The problem of color image enhancement and the specific case of color demosaicing which involves reconstruction of color images from sampled images, is an under-constrained problem. Using single-channel restoration techniques on each color- channel separately results in poorly reconstructed images. It has been shown that better results can be obtained by considering the cross-channel correlation. In this paper, a novel approach to demosaicing is presented, using learning schemes based on Artificial Neural Networks. Thus the reconstruction parameters are determined specifically for predefined classes of images. This approach improves results for images of the learned class, since the variability of inputs is constrained (within the image class) and the parameters are robust due to the learning process. Three reconstruction methods are presented in this work. Additionally, a selection method is introduced, which combines several reconstruction methods and applies the best method for each input.