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Everyone has the dream of being in the center of famous art paintings, admired by numerous future generations. However, the dream came true at a huge cost of the painter’s commission in old days. In our paper, another practical choice is provided for everyone to achieve that dream – an automatic portrait oil painter transferring some artistic styles from one single reference painting. To address this...
People wish to own a portrait painting of themselves by Da Vinci. Unfortunately, it is impossible to make this dream come true; nevertheless, it may give us an opportunity by transferring some artistic features from one single reference painting. To address this issue, we propose a joint-domain image stylization approach, particularly for portrait oil paintings. From the view of artistic appreciation,...
The use of strong lighting contrast to accentuate objects and figures in a painting—called Chiaroscuro—is popular among Renaissance painters such as Caravaggio, La Tour and Rembrandt. In this paper, we propose a new metric called LuCo to quantify the extent to which Chiaroscuro is employed by an artist in a painting. This measurement could be used to assess the capability of any system to fulfill...
Sparse prior provides an effective tool for the image reconstruction. However, the sparse coding for independent patches leads to the unstable sparse decomposition. In this paper, we propose a group structured sparse representation model by considering the nonlocal similarity. The nonlocal similar patches are collected and classified into groups. Patches in the same group are reconstructed based the...
Traditional image stylization is enforced by learning the mappings with an external paired training set. But in practice, people usually encounter a specific stylish image and want to transfer its style to their own pictures without the external dataset. Thus, we propose a hierarchical stylization model with limited reference particularly for oil paintings. First, the edge patch based dictionary is...
Sparse representation provides effective prior information for single-frame super resolution reconstruction. The diversified training samples of the general dictionary lead to the difficulty of recovering fine grained details due to the negligence of redundant structural characteristics. Thus, the dictionary which is adaptive to local structures is needed. Considering the highly structured information...
Zooming in/out appears frequently in video shooting, which makes scale vary between frames. And object motion in videos may cause scale change of the object. It leads to the difficulty in finding similar patches and causes the invalidation of nonlocal means super resolution (NLM SR). In this paper, we propose a novel scale-compensated NLM SR algorithm. First, by considering the parameter model, the...
This paper presents a novel saliency-modulated sparse representation algorithm for image super resolution. In images, regions salient to human eyes appear to be more organized and structured. This property is utilized in both the dictionary learning and the sparse coding process to capture more structural details for the reconstructed image. Apart from a general dictionary, example patches from the...
The observed images are usually noisy due to data acquisition and transmission process. Therefore, image denoising is a necessary procedure prior to post-processing applications. The proposed algorithm exploits the self-similarity based low rank technique to approximate the real-world image in the multivariate analysis sense. It consists of two successive steps: adaptive dimensionality reduction of...
In this paper, we propose a novel algorithm for multi-frame super resolution (SR) with consideration of scale changing between frames. First, we detect the scale of each frame by scale-detector. Based on the scale gap between adjacent frames, we extract patches and modify them from different scales into the same scale to obtain more redundant information. Finally, a reconstruction approach based on...
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