Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on edge optimization is proposed. The experimental results show that the clarity of the output image, the maintenance of facial features, and the color processing of the image are enhanced best by the image translation model based on the generative adversarial network. Finally, the results are compared with other existing methods. Analyzing the experimental results shows that the color face image generated by our method is closer to the target image, and has achieved a better performance in term of Structural Similarity (SSIM)

ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos

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Image abstraction painting of flow-like stylization

U radu se predstavlja tehnika ne-fotorealističkog prikaza u kojoj se iz fotografije dobiva stilizirana apstraktna slika s tonovima koji se prelijevaju. Zasnovana na Kuwahara filtrima i integralnim spiralnim linijama, naša metoda simultano apstrahira oblike i boje, zadržavajući u isto vrijeme osnovna obilježja slika. Posebno razvijamo metodu proširenja i detekcije ruba i usmjeravamo pažnju na specifična obilježja i rubne dijelove slike. Predloženi je algoritam promjenljiv i iterativan te se stupanj prelijevanja tonova i apstrakcije može regulirati. Eksperimentalni rezultati pokazuju da je učinkovitost naše metode u postizanju koherentne i stilizirane apstrakcije zadovoljavajuća, uz zadržavanje osnovnih obilježja iz zadanih fotografija.This paper presents a non-photorealistic rendering technique for producing flow-like abstraction stylization from a photograph. Based on anisotropic Kuwahara filtering in conjunction with line integral convolution, our method abstracts shapes and colors simultaneously while preserving features of images. In particular, we develop an edge detection and dilation method, to draw attention to salient features and image boundaries. This proposed algorithm is incremental and iterative, and therefore the degree of flow and abstraction can be controlled. Experimental results demonstrate that the effectiveness of our method in producing a coherent and flow-like abstraction stylization is satisfactory yet preserving features and directions from photographs

Artistic rendering enhancing global structure

Non-photorealistic rendering techniques usu- ally produce abstracted images. Most existing methods consider local rendering primitives, and global struc- tures may be easily obscured. Inspired by artists, we propose a novel image abstraction method that con- siders preserving or even enhancing global structures in the input images. Linear structures are particularly considered due to their wide existence and the avail- ability of techniques for their reliable detection. Based on various computer vision techniques, the algorithm is fully automatic. As demonstrated in the paper, artistic looking results are obtained for various types of images. The technique is orthogonal to many non-photorealistic rendering techniques and can be combined with them

Artistic minimal rendering with lines and blocks

Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination of refined lines and blocks (potentially simplified), as well as a small number of tones, to produce abstracted artistic rendering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and 2-tone drawings, and use semantic information to improve renderings for faces. By changing some intuitive parameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study

Artistic minimal rendering with lines and blocks

Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination of refined lines and blocks (potentially simplified), as well as a small number of tones, to produce abstracted artistic rendering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and 2-tone drawings, and use semantic information to improve renderings for faces. By changing some intuitive parameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study

Wholetoning: Synthesizing Abstract Black-and-White Illustrations

Black-and-white imagery is a popular and interesting depiction technique in the visual arts, in which varying tints and shades of a single colour are used. Within the realm of black-and-white images, there is a set of black-and-white illustrations that only depict salient features by ignoring details, and reduce colour to pure black and white, with no intermediate tones. These illustrations hold tremendous potential to enrich decoration, human communication and entertainment. Producing abstract black-and-white illustrations by hand relies on a time consuming and difficult process that requires both artistic talent and technical expertise. Previous work has not explored this style of illustration in much depth, and simple approaches such as thresholding are insufficient for stylization and artistic control. I use the word wholetoning to refer to illustrations that feature a high degree of shape and tone abstraction. In this thesis, I explore computer algorithms for generating wholetoned illustrations. First, I offer a general-purpose framework, “artistic thresholding”, to control the generation of wholetoned illustrations in an intuitive way. The basic artistic thresholding algorithm is an optimization framework based on simulated annealing to get the final bi-level result. I design an extensible objective function from our observations of a lot of wholetoned images. The objective function is a weighted sum over terms that encode features common to wholetoned illustrations. Based on the framework, I then explore two specific wholetoned styles: papercutting and representational calligraphy. I define a paper-cut design as a wholetoned image with connectivity constraints that ensure that it can be cut out from only one piece of paper. My computer generated papercutting technique can convert an original wholetoned image into a paper-cut design. It can also synthesize stylized and geometric patterns often found in traditional designs. Representational calligraphy is defined as a wholetoned image with the constraint that all depiction elements must be letters. The procedure of generating representational calligraphy designs is formalized as a “calligraphic packing” problem. I provide a semi-automatic technique that can warp a sequence of letters to fit a shape while preserving their readability

Colour videos with depth : acquisition, processing and evaluation

The human visual system lets us perceive the world around us in three dimensions by integrating evidence from depth cues into a coherent visual model of the world. The equivalent in computer vision and computer graphics are geometric models, which provide a wealth of information about represented objects, such as depth and surface normals. Videos do not contain this information, but only provide per-pixel colour information. In this dissertation, I hence investigate a combination of videos and geometric models: videos with per-pixel depth (also known as RGBZ videos). I consider the full life cycle of these videos: from their acquisition, via filtering and processing, to stereoscopic display. I propose two approaches to capture videos with depth. The first is a spatiotemporal stereo matching approach based on the dual-cross-bilateral grid – a novel real-time technique derived by accelerating a reformulation of an existing stereo matching approach. This is the basis for an extension which incorporates temporal evidence in real time, resulting in increased temporal coherence of disparity maps – particularly in the presence of image noise. The second acquisition approach is a sensor fusion system which combines data from a noisy, low-resolution time-of-flight camera and a high-resolution colour video camera into a coherent, noise-free video with depth. The system consists of a three-step pipeline that aligns the video streams, efficiently removes and fills invalid and noisy geometry, and finally uses a spatiotemporal filter to increase the spatial resolution of the depth data and strongly reduce depth measurement noise. I show that these videos with depth empower a range of video processing effects that are not achievable using colour video alone. These effects critically rely on the geometric information, like a proposed video relighting technique which requires high-quality surface normals to produce plausible results. In addition, I demonstrate enhanced non-photorealistic rendering techniques and the ability to synthesise stereoscopic videos, which allows these effects to be applied stereoscopically. These stereoscopic renderings inspired me to study stereoscopic viewing discomfort. The result of this is a surprisingly simple computational model that predicts the visual comfort of stereoscopic images. I validated this model using a perceptual study, which showed that it correlates strongly with human comfort ratings. This makes it ideal for automatic comfort assessment, without the need for costly and lengthy perceptual studies