381 research outputs found
Accelerating local laplacian filters on FPGAs
Images when processed using various enhancement techniques often lead to edge
degradation and other unwanted artifacts such as halos. These artifacts pose a
major problem for photographic applications where they can denude the quality
of an image. There is a plethora of edge-aware techniques proposed in the field
of image processing. However, these require the application of complex
optimization or post-processing methods. Local Laplacian Filtering is an
edge-aware image processing technique that involves the construction of simple
Gaussian and Laplacian pyramids. This technique can be successfully applied for
detail smoothing, detail enhancement, tone mapping and inverse tone mapping of
an image while keeping it artifact-free. The problem though with this approach
is that it is computationally expensive. Hence, parallelization schemes using
multi-core CPUs and GPUs have been proposed. As is well known, they are not
power-efficient, and a well-designed hardware architecture on an FPGA can do
better on the performance per watt metric. In this paper, we propose a hardware
accelerator, which exploits fully the available parallelism in the Local
Laplacian Filtering algorithm, while minimizing the utilization of on-chip FPGA
resources. On Virtex-7 FPGA, we obtain a 7.5x speed-up to process a 1 MB image
when compared to an optimized baseline CPU implementation. To the best of our
knowledge, we are not aware of any other hardware accelerators proposed in the
research literature for the Local Laplacian Filtering problem.Comment: 6 pages, 5 figures, 2 table
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
Fast Local Laplacian Filters: Theory and Applications
International audienceMulti-scale manipulations are central to image editing but they are also prone to halos. Achieving artifact-free results requires sophisticated edge- aware 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 on gray-scale images that yields speed-ups on the order of 50×. 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
Entropy-Adaptive Filtering
This publication describes an entropy-adaptive filtering, which reduces compression artifacts for videos of any given complexity and at any given video-encoding bit-rate. Unlike other video filtering designs, the entropy-adaptive filtering minimizes the likelihood of compression artifacts by reducing the entropy level of the input video
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
A multi-view approach to cDNA micro-array analysis
The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research
Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences
under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China
under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany
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