2,292 research outputs found
Fast unsupervised multiresolution color image segmentation using adaptive gradient thresholding and progressive region growing
In this thesis, we propose a fast unsupervised multiresolution color image segmentation algorithm which takes advantage of gradient information in an adaptive and progressive framework. This gradient-based segmentation method is initialized by a vector gradient calculation on the full resolution input image in the CIE L*a*b* color space. The resultant edge map is used to adaptively generate thresholds for classifying regions of varying gradient densities at different levels of the input image pyramid, obtained through a dyadic wavelet decomposition scheme. At each level, the classification obtained by a progressively thresholded growth procedure is combined with an entropy-based texture model in a statistical merging procedure to obtain an interim segmentation. Utilizing an association of a gradient quantized confidence map and non-linear spatial filtering techniques, regions of high confidence are passed from one level to another until the full resolution segmentation is achieved. Evaluation of our results on several hundred images using the Normalized Probabilistic Rand (NPR) Index shows that our algorithm outperforms state-of the art segmentation techniques and is much more computationally efficient than its single scale counterpart, with comparable segmentation quality
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Total variation (TV) is a powerful regularization method that has been widely
applied in different imaging applications, but is difficult to apply to diffuse
optical tomography (DOT) image reconstruction (inverse problem) due to complex
and unstructured geometries, non-linearity of the data fitting and
regularization terms, and non-differentiability of the regularization term. We
develop several approaches to overcome these difficulties by: i) defining
discrete differential operators for unstructured geometries using both finite
element and graph representations; ii) developing an optimization algorithm
based on the alternating direction method of multipliers (ADMM) for the
non-differentiable and non-linear minimization problem; iii) investigating
isotropic and anisotropic variants of TV regularization, and comparing their
finite element- and graph-based implementations. These approaches are evaluated
on experiments on simulated data and real data acquired from a tissue phantom.
Our results show that both FEM and graph-based TV regularization is able to
accurately reconstruct both sparse and non-sparse distributions without the
over-smoothing effect of Tikhonov regularization and the over-sparsifying
effect of L regularization. The graph representation was found to
out-perform the FEM method for low-resolution meshes, and the FEM method was
found to be more accurate for high-resolution meshes.Comment: 24 pages, 11 figures. Reviced version includes revised figures and
improved clarit
Nonlinear Spectral Geometry Processing via the TV Transform
We introduce a novel computational framework for digital geometry processing,
based upon the derivation of a nonlinear operator associated to the total
variation functional. Such operator admits a generalized notion of spectral
decomposition, yielding a sparse multiscale representation akin to
Laplacian-based methods, while at the same time avoiding undesirable
over-smoothing effects typical of such techniques. Our approach entails
accurate, detail-preserving decomposition and manipulation of 3D shape geometry
while taking an especially intuitive form: non-local semantic details are well
separated into different bands, which can then be filtered and re-synthesized
with a straightforward linear step. Our computational framework is flexible,
can be applied to a variety of signals, and is easily adapted to different
geometry representations, including triangle meshes and point clouds. We
showcase our method throughout multiple applications in graphics, ranging from
surface and signal denoising to detail transfer and cubic stylization.Comment: 16 pages, 20 figure
Transform recipes for efficient cloud photo enhancement
Cloud image processing is often proposed as a solution to the limited computing power and battery life of mobile devices: it allows complex algorithms to run on powerful servers with virtually unlimited energy supply. Unfortunately, this overlooks the time and energy cost of uploading the input and downloading the output images. When transfer overhead is accounted for, processing images on a remote server becomes less attractive and many applications do not benefit from cloud offloading. We aim to change this in the case of image enhancements that preserve the overall content of an image. Our key insight is that, in this case, the server can compute and transmit a description of the transformation from input to output, which we call a transform recipe. At equivalent quality, our recipes are much more compact than JPEG images: this reduces the client's download. Furthermore, recipes can be computed from highly compressed inputs which significantly reduces the data uploaded to the server. The client reconstructs a high-fidelity approximation of the output by applying the recipe to its local high-quality input. We demonstrate our results on 168 images and 10 image processing applications, showing that our recipes form a compact representation for a diverse set of image filters. With an equivalent transmission budget, they provide higher-quality results than JPEG-compressed input/output images, with a gain of the order of 10 dB in many cases. We demonstrate the utility of recipes on a mobile phone by profiling the energy consumption and latency for both local and cloud computation: a transform recipe-based pipeline runs 2--4x faster and uses 2--7x less energy than local or naive cloud computation.Qatar Computing Research InstituteUnited States. Defense Advanced Research Projects Agency (Agreement FA8750-14-2-0009)Stanford University. Stanford Pervasive Parallelism LaboratoryAdobe System
Fast and Robust Pyramid-based Image Processing
Multi-scale manipulations are central to image editing but they are also prone to halos. Achieving artifact-free results requires sophisticated edgeaware techniques and careful parameter tuning. These shortcomings were recently addressed by the local Laplacian filters, which can achieve a broad range of effects using standard Laplacian pyramids. However, these filters are slow to evaluate and their relationship to other approaches is unclear. In this paper, we show that they are closely related to anisotropic diffusion and to bilateral filtering. Our study also leads to a variant of the bilateral filter that produces cleaner edges while retaining its speed. Building upon this result, we describe an acceleration scheme for local Laplacian filters that yields speed-ups on the order of 50x. Finally, we demonstrate how to use local Laplacian filters to alter the distribution of gradients in an image. We illustrate this property with a robust algorithm for photographic style transfer
Low-Light Enhancement in the Frequency Domain
Decreased visibility, intensive noise, and biased color are the common
problems existing in low-light images. These visual disturbances further reduce
the performance of high-level vision tasks, such as object detection, and
tracking. To address this issue, some image enhancement methods have been
proposed to increase the image contrast. However, most of them are implemented
only in the spatial domain, which can be severely influenced by noise signals
while enhancing. Hence, in this work, we propose a novel residual recurrent
multi-wavelet convolutional neural network R2-MWCNN learned in the frequency
domain that can simultaneously increase the image contrast and reduce noise
signals well. This end-to-end trainable network utilizes a multi-level discrete
wavelet transform to divide input feature maps into distinct frequencies,
resulting in a better denoise impact. A channel-wise loss function is proposed
to correct the color distortion for more realistic results. Extensive
experiments demonstrate that our proposed R2-MWCNN outperforms the
state-of-the-art methods quantitively and qualitatively.Comment: 8 page
- …