Noppadol Chumchob

A Fourth-Order Variational Image Registration Model and Its Fast Multigrid Algorithm

Several partial differential equations (PDEs) based variational methods can be used for deformable image registration, mainly differing in how regularization for deformation fields is imposed [J. Modersitzki, Numerical Methods for Image Restoration, Oxford University Press, Oxford, 2004]. On one hand for smooth problems, models of elastic-, diffusion-, and fluid-image registration are known to generate globally smooth and satisfactory deformation fields. On the other hand for nonsmooth problems, models based on the total variation (TV) regularization are better for preserving discontinuities of the deformation fields. It is a challenge to design a deformation model suitable for both smooth and nonsmooth deformation problems. One promising model that is based on a curvature type regularizer and appears to deliver excellent results for both problems is proposed and studied in this paper. A related work due to B. Fischer and J. Modersitzki [J. Math. Imaging Vision, 18 (2003), pp. 81–85] and then refined by S...

A new variational model for removal of combined additive and multiplicative noise and a fast algorithm for its numerical approximation

Variational image restoration models for both additive and multiplicative noise (MN) removal are rarely encountered in the literature. This paper proposes a new variational model and a fast algorithm for its numerical approximation to remove independent additive and MN from digital images. Two previous works by L. Rudin, S. Osher, and E. Fatemi [Nonlinear total variation based noise removal algorithms, Phys. D 60 (1992), pp. 259–268] and Z. Jin and X. Yang [Analysis of a new variational model for multiplicative noise removal, J. Math. Anal. Appl. 362 (2010), pp. 415–426] are used to develop the new model. As a result, developing a fast numerical algorithm is difficult because the associated Euler–Lagrange equation is highly nonlinear and standard unilevel iterative methods are not appropriate. To this end, we develop an efficient nonlinear multigrid approach via a robust fixed-point smoother. Numerical tests using both synthetic and realistic images not only confirm that our new model delivers quality results but also that the proposed numerical algorithm allows a very fast numerical realization of the model.

Vectorial Total Variation-Based Regularization for Variational Image Registration

To use interdependence between the primary components of the deformation field for smooth and non-smooth registration problems, the channel-by-channel total variation- or standard vectorial total variation (SVTV)-based regularization has been extended to a more flexible and efficient technique, allowing high quality regularization procedures. Based on this method, this paper proposes a fast nonlinear multigrid (NMG) method for solving the underlying Euler-Lagrange system of two coupled second-order nonlinear partial differential equations. Numerical experiments using both synthetic and realistic images not only confirm that the recommended VTV-based regularization yields better registration qualities for a wide range of applications than those of the SVTV-based regularization, but also that the proposed NMG method is fast, accurate, and reliable in delivering visually-pleasing registration results.

GPU-based total variation image restoration using Sliding Window Gauss-Seidel algorithm

Image restoration has been a research topic deeply investigated within the last two decades. As is well-known, total variation (TV) minimization by Rudin, Osher, and Fatami [6] offers superior image restoration quality and involves solving a second order nonlinear partial differential equation (PDE). In more recent years, some effort has been made in improving computational speed for solving the associated PDE remained a bottleneck, preventing its applications to high-resolution digital images. In this paper, we improve a novel parallel algorithm Gauss-Seidel on GPU, called QL-SWGS. The algorithm is improved from the original Sliding Window Gauss Seidel proposed in [1]. As expected, our numerical results on realistic and synthetic images not only confirm that the proposed algorithm on GPU delivers quality results but also that it is many orders of magnitude faster than those algorithms on multicore CPU, particularly by at most 80% from our benchmark.

An efficient asynchronous approach for Gauss-Seidel iterative solver for FDM/FEM equations on multi-core processors

In this paper, we proposed a new parallel iterative asynchronous method for Gauss-Seidel and Successive Over-Relaxation (SOR) for finite difference method (FDM) and finite element method (FEM). The approach attempts to minimize the thread synchronization which incurs a lot of thread idle time due to the dependency of computation. Our proposed method maximizes the thread utilization on multi-core processors with some space requirement for storing current states. We implement our proposed method based on the Poissons equation with FDM. It is found that our proposed algorithm runs 5.88 times faster than the original Gauss-Seidel and achieve speedup up to 1.25 compared with the parallel Sliding Window version.

Real-time video denoising for 2D ultrasound streaming video on GPUs

The ultrasound videos are mainly contaminated by multiplicative noises but also contaminated with additive noises. As the past few decades, there are some studies to remove the noises from ultrasound images as in the JY model [1] and the variational model which removes both types of noises. However, denoising these noises from the ultrasound video is the time-consuming process. With the advancement of multi-core and many-core processors, it makes the denoising process much faster and it is possible to render while doing the real-time denoising. In this study, we propose the modified strategy from [2] to denoise the streaming ultrasound video in real-time. Our proposed model can retain the frame order, and get the satisfactory frame rate (about 14.98 fps). The proposed strategy boosts the speedup of the frame denoising to 3.79 times compared to the sequential computation.

Real-time parallel spatial video denoising schemes on multi-core processors

Noises in videos can occur during recording and transmission. The advancement of the processor technology makes real time video denoising possible on multicore processors. In this paper, we investigate parallel techniques for denoising the real-time video on a multi-core processor. We compare two strategies: a block strategy, which assigns a group of threads to each block of video frames and a distributor strategy, which uses one thread to distribute the frame data to each thread. From our experiments with the total variation based image denoising technique, we found that by using the distributor strategy, we can achieve speedup which is 1.28 times faster than the block strategy and the video frame rate can be increased by 18.44%.

Two Parallel Strategies for Real-time Spatial Video Denoising for Multi-core Processors

Video denoising is usually a time consuming process especially for large video files. With the advancement of the processor technology, it is possible to perform video denoising in real-time on multi-core processors. In this paper, we study parallel techniques for denoising real-time video on multi-core processor which work on both shared memory model and distributed memory model. We investigate two approaches: a block approach, which assigns a group of threads to each block of video frames; and a distributor approach, which uses one thread to distribute the frame data to each thread. Our experiments focus on the image denoising technique based on the total variation but the approach can be integrated with other image denoising algorithm like discrete wavelet transform (DWT) or diffusion technique. We found that by using the distributor strategy, we can achieve speedup which is 1.27 times faster than the block strategy and the video frame rate can be increased by 7.43%. Moreover, we also apply the prefetching technique which further enhance frame rate by 22.02% and frame rate control to stabilize frame rate and retain the original video length during denoising and playing in real-time. Our method also has good denoised quality which is better than previous work in [1] in average case. General Terms Algorithms, Framework, Image Processing, Video Processing, High Performance Computing, Parallel Computing Keywords video denoising, parallel computing, OpenMP, ROF model, total variation.

Utilising the pipeline framework and state-based non-linear Gauss-Seidel for large satellite image denoising based on CPU-GPU cores

Satellite images are usually large and are contaminated with noises during the acquisition process. Typically, they are composed of both additive noises and multiplicative noises. Denoising such images requires numerical processes that are time-consuming. In this paper, we propose a framework for denoising both multiplicative and additive noises at the same time based on the modern denoising technique in Chumchob et al. 2013. Our framework is able to fully utilise all available computing units both CPU cores and GPU cores effectively. We carefully divide the computation into stages which allows the computing units to work on each data partition in a pipeline fashion and tested our framework with different chunk sizes from 256 × 256 to 1024 × 1024. The experiments show that the speedup for the chunk size of 2048 × 2048 can be up to 70.98 times comparing with the normal denoising algorithm. Moreover, we also made the modification of stated-based Gauss-Seidel from Dolwithayakul et al. 2012 be suitable for GPU. We also change data structure to avoid usage of pointer and implement the memory hierarchy to reduce the single point of synchronisation and guarantee mutual exclusion on the job table.