Abstract: Stereo matching is one of the key techniques in stereo vision. Due to the existing adaptive window stereo matching algorithm is insufficient to extract features in low-texture regions, a weighted dynamic adaptive window stereo matching algorithm based on pixel gradient value is proposed. Firstly, the Sobel operator is used to calculate the gradient value of each pixel in the image and the phase information is introduced. According to the two, the pixel is divided into strong, medium and weak texture regions, and then different thresholds are assigned to the pixels in different regions. Then the image is converted from the RGB color space to the HSV color space and the matching window is dynamically generated according to the region threshold and color threshold. The HAD cost calculation function was established and the traditional Census algorithm was improved. The disparity map was obtained by nonlinear fusion calculation, and the obtained disparity map was detected by subpixel and filtered by median value. Finally, the high-precision disparity map was obtained. Experimental results show that the proposed algorithm is effective, has high matching accuracy, and has good robustness to optical distortion and edge information conditions.
Image deblurring and inpainting are traditional image processing problems, and the effects achieved for high-resolution images are not satisfactory. In recent years, Convolutional Sparse coding (CSC) has been received more attention and introduced into image processing, such as blind deblurring. However, none of the works address the issue containing both blur and inpainting. In this work, we propose a novel framework of CSC for simultaneous image deblurring and inpainting. First, we learn a dictionary instead of applying a given dictionary for better image representation. Second, we use the learned dictionary with the ℓ1 norm to regularize images. In addition, we apply a total anisotropic variation to enhance the edges of the image. Usually, we use the alternating direction method of multipliers (ADMM) formulation in the Fourier domain for the dictionary. We demonstrate the proposed training scheme for simultaneous image deblurring and inpainting, achieving state-of-the-art results.
In this paper, we address the rain streak removal from a single image. In order to efficiently detect and remove the annoying rain streaks, we propose a global single-directional gradient prior with the L0 norm to model the rain streak. To preserve the abundant information of the background, we learn a convolutional sparse coding (CSC) to represent the background. Furthermore, we develop an alternating direction method of multipliers (ADMM) to solve multi-variable optimization problems. Experiments on synthesized and real-world images show that the proposed method outperforms state-of-art methods in terms of rain streak removal and background preservation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.