Dictionary Replacement for Single Image Restoration of 3D Scenes
نویسندگان
چکیده
In this paper, we address the problem of jointly estimating the latent image and the depth/blur map from a single space-variantly blurred image using dictionary replacement. While most of the dictionary-based deblurring methods consider planar scenes with spaceinvariant blur, we handle 3D scenes with spacevariant blur caused by either camera motion or optical defocus. For a given blurred image, the dictionary blurred with the corresponding blur kernel provides the best representation with the least error. We formulate our problem of blur map and latent image estimation as a multi-label MRF and solve it using graph-cut. An image X degraded by space-invariant blur h can be modeled by convolution as
منابع مشابه
Dictionary Replacement for Single Image Restoration of 3D Scenes
In this paper, we address the problem of jointly estimating the latent image and the depth/blur map from a single space-variantly blurred image using dictionary learning. The approach taken is based on the central idea of dictionary replacement viz. the sparse representation of a blurred image over a blurred dictionary is equivalent to that over a clean dictionary. While most of the dictionary-...
متن کاملDiving into Haze-Lines: Color Restoration of Underwater Images
Underwater images suffer from color distortion and low contrast, because light is attenuated as it propagates through water. The attenuation varies with wavelength and depends both on the properties of the water body in which the image was taken and the 3D structure of the scene, making it difficult to restore the colors. Existing single underwater image enhancement techniques either ignore the...
متن کاملSparse Representations for Three-Dimensional Range Data Restoration
In this paper, the problem of denoising and occlusion restoration of 3D range data based on dictionary learning and sparse representation methods for image denoising is explored. We apply these techniques after converting the noisy 3D surface into one or more images. We present experimental results on the proposed approaches.
متن کاملA Stable Method Solving the Total Variation Dictionary Model with L Constraints
Image restoration plays an important role in image processing, and numerous approaches have been proposed to tackle this problem. This paper presents a modified model for image restoration, that is based on a combination of Total Variation (TV) and Dictionary approaches. Since the well-known TV regularization is non-differentiable, the proposed method utilizes its dual formulation instead of it...
متن کامل3D Scene and Object Classification Based on Information Complexity of Depth Data
In this paper the problem of 3D scene and object classification from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic information theory, a new def...
متن کامل