We propose a image-to-painting translation method that can generate paintings on a stroke-by-stroke basis. Unlike previous pixel-to-pixel or sequential optimization methods, our method generates a set of physically meaningful stroke parameters, which are closer to the way humans draw. These parameters can be further rendered using a renderer. We add an attention mechanism network to the proposed renderer to improve the quality of the painting images and use smooth L1 loss in the training process of the renderer to make the model converge faster. Our method can join neural style transfer, and we used Visual Geometry Group perceptual loss in the neural style transfer stage to get more realistic results. The experimental results show that the renderer used in our method is better than other renderers, and peak signal-to-noise ratio evaluation metrics are improved by 4.9% compared with previous renderers.
In this paper, a compressing and reconstruction method for a noise video based on Compressed Sensing (CS) theory is proposed. At first, the CS theory is presented. Then the noise video is estimated from noisy measurement by solving the convex minimization problem. The video recovery algorithms based on gradient-based method is used to compressing and reconstructing the noise signal. And a compressive sensing algorithm with gradient-based method is proposed. At last, the performance of the proposed approach is shown and compared with some conventional algorithms. Our method can obtain best results in terms of peak signal noise ratio (PSNR) than those achieved by common methods with only a little runtime.
In this paper, a denoise approach is proposed to reduce the speckle noise in SAR images
based on compress sensing. Through the skill of compressed sensing, we divide the image into some
blocks, and propose an image reconstruction method based on block compressing sensing with
Orthogonal Matching Pursuit. By adding some simulated speckle noise in the SAR image, the
performance of the proposed approach is shown and compared with a conventional algorithm. the
result has been shown that our method can get better result in terms of peak signal noise ratio (PSNR).
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