Image Smoothing Technique Research Articles

Contrast-preserving image smoothing via the truncated first-order rational function

The main task of image smoothing is to remove the insignificant details of the input image while preserving salient structural edges. In the fields of computer vision and graphics, image smoothing techniques are of great practical importance. In this paper, we investigate a new nonconvex variational optimization model for contrast-preserving image smoothing based on the truncated first-order rational (TFOR) penalty function. We employ an iterative numerical method that utilizes the half-quadratic minimization to effectively solve the proposed model. To validate the effectiveness of the proposed method, we compare it with some related state-of-the-art methods. Experimental results are given to show that the proposed method performs well in preserving the image contrast while maintaining the important edges and structures. We apply the proposed method on various classic image processing tasks such as clip-art compression artifact removal, detail enhancement, image denoising, image abstraction, flash and no-flash image restoration, and guided depth map upsampling.

Security analysis on optical cryptosystem based on interference and phase-retrieval technique

In this paper, the security strength of an optical cryptosystem based on interference and phase-retrieval technique has been evaluated. Compared to the conventional interference-based scheme in which private keys are generated according to the principle of vector decomposition, a modified error-reduction phase retrieval algorithm is employed to produce a plaintext-dependent key with the smaller size than that of the plaintext in the improved cryptosystem; thus, resulting in higher resistance against iterative attacks and lower burden of data transmission. However, it is found that only one phase-only mask (POM) served as the private key is unknown to illegal users, the key space is limited; thus, it is possible to investigate an iterative process to recover the unknown key with the aid of public keys and the arbitrarily given ciphertext as additional constraints. And employing the recovered private key, encoded information may be reconstructed. Based on this cryptoanalysis, the iterative ciphertext-only attack (COA) with normalization and image smoothing techniques (i.e., the median filter, the bilateral filter) is proposed to crack the enhanced scheme. Numerical simulations are carried out to validate the feasibility and effectiveness of the proposed COA.

Overview of Image Smoothing Algorithms

In the process of image generation and transmission, it is easy to be polluted and affected by various kinds of noise, which reduces the quality of the image. In order to avoid the adverse effects of image processing and image visual effect, we can use image smoothing technology to improve the adverse effects, so as to make the image clearer. There are a variety of image smoothing techniques, which had great smoothing effect, but there are also different defects. Based on the analysis of different noise models, this paper uses mean smoothing, median smoothing, Gaussian smoothing, bilateral filtering and other techniques to study and analyse the image processing algorithm, the implementation method and efficiency, which provides efficient theoretical value for image processing and image visual effect in various fields involved in image processing.

Path-Based Analysis for Structure-Preserving Image Filtering

Structure-preserving image filtering is an image smoothing technique that aims to preserve prominent structures while removing unwanted details in natural images. However, relevant studies mainly focus on small variances/fluctuations suppression and are vulnerable to separate pixels connected by some low-contrast edges or cluster pixels which exhibit strong differences between neighbors in highly textured region. Inspired by the fact that the human visual system significantly outperforms manually designed operators in extracting meaningful structures from natural scenes, we present an efficient structure-preserving filtering method which integrates similarity, proximity and continuation principles of human perception to accomplish high-contrast details (textures/noises) smoothing. Additionally, a Liebig’s law of minimum-based distance transform is presented to seamlessly incorporate the three properties for the description of the filter kernel. Experiments demonstrate that our distance transform keeps a clustering-like manner of separating different image pixels and grouping similar ones with the awareness of structure. When integrating this affinity measure into the bilateral-filter-like framework, our method can efficiently remove high-contrast textures/noises while preserving major structures.

Finite-Difference Time-Domain Modeling for Electromagnetic Wave Analysis of Human Voxel Model at Millimeter-Wave Frequencies

The finite-difference time-domain (FDTD) modeling of a human voxel model at millimeter-wave (mmWave) frequencies is presented. It is very important to develop the proper geometrical and electrical modeling of a human voxel model suitable for accurate electromagnetic (EM) analysis. Although there are many human phantom models available, their voxel resolution is too poor to use for the FDTD study of EM wave interaction with human tissues. In this paper, we develop a proper human voxel model suitable for mmWave FDTD analysis using the voxel resolution enhancement technique and the image smoothing technique. The former can improve the resolution of the human voxel model and the latter can alleviate staircasing boundaries of the human voxel model. Quadratic complex rational function is employed for the electrical modeling of human tissues in the frequency range of 6–100 GHz. Massage passing interface-based parallel processing is also applied to dramatically speed up FDTD calculations. Numerical examples are used to illustrate the validity of the mmWave FDTD simulator developed here for bio electromagnetics studies.

An iterative possibilistic knowledge diffusion approach for blind medical image segmentation

This paper presents an image segmentation method imitating human focusing visual attention in image interpretation using possibilistic knowledge modeling concepts. The proposed pixel level method consists on the Iterative Possibilistic Knowledge Diffusion (IPKD) on immediate neighbourhood pixels. The advantage of this mechanism is to provide iterative diffusion of per-pixel certain knowledge to surrounding pixels in order to progressively refine the segmentation process. The diffusion process is achieved using image smoothing techniques such as Nagao and Gabor filtering, mean filtering and anisotropic diffusion. Those diffusion techniques are then compared in the possibilistic knowledge representation space. The merit of a possibilistic knowledge representation, rather than a grey-level sensor based representation, is demonstrated by both experimental and synthetic data. Producing the lowest error rates, possibilistic knowledge diffusion using Nagao filter is adopted for the approach assessment. Experimental results using synthetic images as well as mammographic images from MIAS (Mammographic Image Analysis Society) data-base, are performed in order to assess the efficiency of the proposed segmentation method according to the visual criterion as well as some quantitative criteria. IPKD's performance (in terms of recognition rate, 94.37% and global predictive rate, 92.18%) is compared with three relevant reference methods: level-set, Fuzzy C-Mean and region growing methods. The IPKD approach outperforms the other three methods, respectively, at the recognition rates of 89.77%, 84.43% and 88.11% and at the global predictive rates of 87.86%, 89.72% and 84.04%. Noise-sensitivity experiments have been conducted on synthetic as well as on real images. The proposed IPKD approach outperforms the three reference methods and in addition, exhibits a desired stability behaviour.

GPU Implementation of Image Convolution Using Sparse Model with Efficient Storage Format

With the growth of data parallel computing, role of GPU computing in non-graphic applications such as image processing becomes a focus in research fields. Convolution is an integral operation in filtering, smoothing and edge detection. In this article, the process of convolution is realized as a sparse linear system and is solved using Sparse Matrix Vector Multiplication (SpMV). The Compressed Sparse Row (CSR) format of SPMV shows better CPU performance compared to normal convolution. To overcome the stalling of threads for short rows in the GPU implementation of CSR SpMV, a more efficient model is proposed, which uses the Adaptive-Compressed Row Storage (A-CSR) format to implement the same. Using CSR in the convolution process achieves a 1.45x and a 1.159x increase in speed compared to the normal convolution of image smoothing and edge detection operations, respectively. An average speedup of 2.05x is achieved for image smoothing technique and 1.58x for edge detection technique in GPU platform usig adaptive CSR format.

Entropy-based bilateral filtering with a new range kernel

Bilateral filter (BF) is a well-known edge-preserving image smoothing technique, which has been widely used in image denoising. The major drawback of BF is that its range kernel is sensitive to noise. To address this issue, we propose an entropy-based BF (EBF) with a new range kernel which contains a new range distance. The new range distance is robust to noise by exploiting the information from the denoised estimate and the corresponding method noise, i.e., the difference between the noisy image and its denoised estimate. Moreover, in order to consider the local statistics of images, local entropy is applied to adaptively guide the range parameter selections. This allows our method to adapt to the images with different characteristics. Experimental results demonstrate that the proposed EBF significantly outperforms the standard BF in terms of both quantitative metrics and subjective visual quality.

Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection

We present the analysis of three independent and most widely used image smoothing techniques on a new fractional based convolution edge detector originally constructed by same authors for image edge analysis. The implementation was done using only Gaussian function as its smoothing function based on predefined assumptions and therefore did not scale well for some types of edges and noise. The experiments conducted on this mask using known images with realistic geometry suggested the need for image smoothing adaptation to obtain a more optimal performance. In this paper, we use the structural similarity index measure and show that the adaptation technique for choosing smoothing function has significant advantages over a single function implementation. The new adaptive fractional based convolution mask can smoothly find edges of various types in detail quite significantly. The method can now trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges.

A Novel TwoStage Impulse Noise Removal Technique based on Neural Networks and Fuzzy Decision

enhancement is plays a vital role in various applications. There are many techniques to remove the noise from the image and produce the clear visual of the image. Moreover, there are several filters and image smoothing techniques available in the literature. All these available techniques have certain limitations. Recently, neural networks are found to be a very efficient tool for image enhancement. A novel two-stage noise removal technique for image enhancement and noise removal is proposed in this paper. In noise removal stage, an adaptive two-level feed forward Neural Network (NN) with a Modified Levenberg-Marquardt training algorithm was used to eliminate the impulse noise. In the image enhancement stage, the fuzzy decision rules inspired by the Human Visual System (HVS) are used to categorize the image pixels into human perception sensitive class and nonsensitive class, and to enhance the quality of the image. The Hyper trapezoidal fuzzy membership function is used in the proposed technique. In order to improve the sensitive regions with higher visual quality, a Neural Network (NN) is proposed. The experiment is conducted with standard image. It is observed from the experimental result that the proposed two stage technique shows significant performance when compared to existing methods.

An edge-based algorithm for discontinuity adaptive color image smoothing

We present a new scheme to increase the performance of edge-preserving image smoothing algorithm from the parameter tuning of a Markov random field (MRF) function. This method is based on an automatic control of the image- smoothing strength in the MRF function in which the parameter control of the function depends on edge magnitude resulted from discontinuities of image intensity. Without any binary decision for the edge magnitude, direct use of full edge magnitude could improve the performance of discontinuity-preserving image smoothing technique.

Review and evaluation of MRI nonuniformity corrections for brain tumor response measurements.

Current MRI nonuniformity correction techniques are reviewed and investigated. Many approaches are used to remedy this artifact, but it is not clear which method is the most appropriate in a given situation, as the applications have been with different MRI coils and different clinical applications. In this work four widely used nonuniformity correction techniques are investigated in order to assess the effect on tumor response measurements (change in tumor volume over time): a phantom correction method, an image smoothing technique, homomorphic filtering, and surface fitting approach. Six brain tumor cases with baseline and follow-up MRIs after treatment with varying degrees of difficulty of segmentation were analyzed without and with each of the nonuniformity corrections. Different methods give significantly different correction images, indicating that rf nonuniformity correction is not yet well understood. No improvement in tumor segmentation or in tumor growth/shrinkage assessment was achieved using any of the evaluated corrections.