Topic
Bilateral filter
About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: Experimental results on the proposed novel longitudinally guided super-resolution algorithm for neonatal MR images demonstrate that the proposed algorithm recovers clear structural details and outperforms state-of-the-art methods both qualitatively and quantitatively.
Abstract: Neonatal magnetic resonance (MR) images typically have low spatial resolution and insufficient tissue contrast. Interpolation methods are commonly used to upsample the images for the subsequent analysis. However, the resulting images are often blurry and susceptible to partial volume effects. In this paper, we propose a novel longitudinally guided super-resolution (SR) algorithm for neonatal images. This is motivated by the fact that anatomical structures evolve slowly and smoothly as the brain develops after birth. We propose a strategy involving longitudinal regularization, similar to bilateral filtering, in combination with low-rank and total variation constraints to solve the ill-posed inverse problem associated with image SR. Experimental results on neonatal MR images demonstrate that the proposed algorithm recovers clear structural details and outperforms state-of-the-art methods both qualitatively and quantitatively.
32 citations
••
TL;DR: To achieve the computation demand of guided filtering in full-HD video, a double integral image architecture for guided filter ASIC design is proposed and a reformation of the guided filter formula is proposed, which can prevent the error resulted from truncation in the fractional part and modify the regularization parameter ε on user's demand.
Abstract: Filtering is widely used in image and video processing for various applications Recently, the guided filter has been proposed and became one of the popular filtering methods In this paper, to achieve the computation demand of guided filtering in full-HD video, a double integral image architecture for guided filter ASIC design is proposed In addition, a reformation of the guided filter formula is proposed, which can prevent the error resulted from truncation in the fractional part and modify the regularization parameter e on user's demand The hardware architecture of the guided image filter is then proposed and can be embedded in mobile devices to achieve real-time HD applications To the best of our knowledge, this paper is also the first ASIC design for guided image filter With a TSMC 90-nm cell library, the design can operate at 100 MHz and support for Full-HD (1920 × 1080) 30 frame/s with 929K gate counts and 32 KB on-chip memory Moreover, for the hardware efficiency, our architecture is also the best compared to other previous works with bilateral filter
32 citations
••
TL;DR: It is demonstrated that adaptive filtering provides improved detection of activated regions and average activities in consistent regions rather than regions that maximize correlation with a BOLD model.
Abstract: We present a class of adaptive filtering techniques of functional magnetic resonance imaging (fMRI) data related to bilateral filtering. This class of methods average activities in consistent regions rather than regions that maximize correlation with a BOLD model. Similarity measures based on signal similarity and anatomical similarity are discussed and compared experimentally to standard linear low pass filtering. It is demonstrated that adaptive filtering provides improved detection of activated regions.
32 citations
•
06 Aug 1998
TL;DR: A hybrid bilinear scaling (Qscale) scheme as discussed by the authors produces output images that have comparable quality to traditional linear interpolation algorithms, but requires a less complex hardware implementation, since only new pixels are computed, the Qscale system is less computationally complex.
Abstract: A hybrid bilinear scaling (Qscale) scheme produces output images that have comparable quality to traditional bilinear interpolation algorithms, but requires a less complex hardware implementation. The Qscale system does not reverse-map output pixels back to arbitrary locations in the input space as defined by the mapping function. Rather all pixel values and locations are calculated after all of the original input pixels are mapped to the output. That is, all of the original image pixels are used “as-is” in the resultant scaled image. New pixels are generated from the original input pixels to meet the desired output pixel dimensions. Because only new pixels are computed, the Qscale system is less computationally complex. The computational requirements are further reduced because new pixels are computed between original pixel pairs meaning only two pixels are involved in the computation. Coefficients can be chosen to be fractional powers of two (0.5, 0.25, 0.125, etc) for the interpolation calculation between pixel pairs. By selecting coefficients this way, the linear computation reduces to a “shift-and-add” operation, which is easily implemented in hardware.
32 citations
••
20 Dec 2008TL;DR: VLSI architectures and FPGA implementation for edge-preserving filter are presented and it is shown that the PSNR improvement is up to 5.3 dB for Gaussian noisy images.
Abstract: In this paper, VLSI architectures and FPGA implementation for edge-preserving filter are presented. We proposed two architectures for edge preserving filter: full parallel pipelined and structure-shared architectures. The edge-preserving filter uses adaptive coefficient mask based on the intensity distance in filter blocks. Compared with the bilateral filter, the proposed edge-preserving filter provides significantly noise reduction. We implement the proposed architecture on Cyclone II EP2C70F896C8 FPGA device from Altera Corp. Our experiments show that the PSNR improvement is up to 5.3 dB for Gaussian noisy images.
32 citations