Guided Image Filtering Research Articles

Low and non-uniform illumination color image enhancement using weighted guided image filtering

In the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.

Color Image Demosaicking using Deep CNN-based Self Ensemble Approach with Guided Image Filtering

Color Image Demosaicking using Deep CNN-based Self Ensemble Approach with Guided Image Filtering

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of with and g. This result increases the precision in the biomass estimation by around compared to previous works.

Fog Reduction in an Images using Global Guided Image Filtering

Under poor weather conditions such as fog, haze, rain, and so on, images of outdoor scenes suffer from faint colour, slight contrasts, and changed luminosity. The contrasts of artefacts and the distortion of air light colour are vastly increased as fog is minimized. The same configuration is used in the editing methods, such as the 'Guided Image Filter' and the 'Weighted Guided Image Filter.' In this article, we suggest a novel approach called Global guided image filter from the Global Channel of Structural sharing and the Global Edge Preservative Smoothing to obtain fine structures and to eliminate the fog in the input image. The structure exchange channel contributions exist an image to distinguish, moreover, a vector path field. The structure remains well-defined by vector path field as well as exchanged for a structure exchange channel to isolate the image by analysis of the experimental effects. In comparison with GIF as well as WGIF, it reveals that a fine structure is maintained.

Color image watermarking based on reflectance component modification and guided image filtering

Digital images available on the Internet can be effortlessly copied and redistributed. Many image watermarking methods have been developed and are used as technical solutions to trace the ownership. Various watermark embedding and extraction techniques were considered and used in order to obtain reliable extraction and robustness against attacks. This paper presents a new color image watermarking method based on modification of the reflectance component in the Hue-Saturation-Value (HSV) color space. In the watermark embedding process, the reflectance component extracted from the S component is modified in accordance with Just Noticeable Difference (JND) thresholds derived from the V component. Guided image filtering is used in watermark extraction to predict the original reflectance component, and blind extraction is achieved by subtracting the predicted component from the watermarked one. The performances of five watermarking methods, including the proposed method, were evaluated and compared for accuracy and robustness at the equivalent quality of watermarked images. The results demonstrate that the proposed method provides an improved quality of extracted watermark by both objective and subjective quality measures. It is also more robust than the previous methods against various types of image processing based attacks, including the Stirmark benchmark.

An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single-image haze removal based on dark channel prior with weighted guided coefficient and union self-adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k-means clustering method is adopted to calibrate the original image into bright and non-bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected-component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.

Sparse-domain regularized stripe decomposition combined with guided-image filtering for ring artifact removal in propagation-based x-ray phase-contrast CT

Propagation-based x-ray phase-contrast computed tomography (PB-PCCT) images often suffer from severe ring artifacts. Ring artifacts are mainly caused by the nonuniform response of detector elements, and they can degrade image quality and affect the subsequent image processing and quantitative analyses. To remove ring artifacts in PB-PCCT images, a novel method combined sparse-domain regularized stripe decomposition (SDRSD) method with guided image filtering (GIF) was proposed. In this method, polar coordinate transformation was utilized to convert the ring artifacts to stripe artifacts. And then considering the directional and sparse properties of the stripe artifacts and the continuity characteristics of the sample, the SDRSD method was designed to remove stripe artifacts. However, for the SDRSD method, the presence of noise may destroy the edges of the stripe artifacts and lead to incomplete decomposition. Hence, a simple and efficient smoothing technique, namely GIF, was employed to overcome this issue. The simulations and real experiments demonstrated that the proposed method could effectively remove ring artifacts as well as preserve the structures and edges of the samples. In conclusion, the proposed method can serve as an effective tool to remove ring artifacts in PB-PCCT images, and it has high potential for promoting the biomedical and preclinical applications of PB-PCCT.

Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain.

Multimodal medical image fusion aims to fuse images with complementary multisource information. In this paper, we propose a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-subsampled contourlet transform (NSCT) domain. Firstly, the source images are decomposed by NSCT, several low- and high-frequency sub-bands are generated. Secondly, the PCNN-based fusion rule is used to process the low-frequency components, and the GIF-WSEML fusion model is used to process the high-frequency components. Finally, the fused image is obtained by integrating the fused low- and high-frequency sub-bands. The experimental results demonstrate that the proposed method can achieve better performance in terms of multimodal medical image fusion. The proposed algorithm also has obvious advantages in objective evaluation indexes VIFF, QW, API, SD, EN and time consumption.

Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.

Computed tomography imaging spectrometry based on superiorization and guided image filtering.

Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging technique that can obtain a three-dimensional (${2D +}\lambda$) data cube of the target scene within a single exposure. Previous studies of CTIS suggest that reconstructions usually suffer from severe artifacts due to the limited number of projections available. To overcome this limitation, an iterative algorithm combining superiorization and guided image filtering is proposed to explore the intrinsic properties of the hyperspectral data cube as well as the characteristics of zero-order diffraction for the first time, to the best of our knowledge. Results from both simulative studies and proof-of-concept experiments demonstrate its superiority in suppressing artifacts and improving precision over the frequently used expectation maximization algorithm.

Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Guided image filtering (GIF) based cost aggregation or disparity refinement stereo matching algorithms are studied extensively owing to the edge-aware preserved smoothing property. However, GIF suffers from halo artifacts in sharp edges and shows high computational costs on high-resolution images. The performance of GIF in stereo matching would be limited by the above two defects. To solve these problems, a novel fast gradient domain guided image filtering (F-GDGIF) is proposed. To be specific, halo artifacts are effectively alleviated by incorporating an efficient multi-scale edge-aware weighting into GIF. With this multi-scale weighting, edges can be preserved much better. In addition, high computational costs are cut down by sub-sampling strategy, which decreases the computational complexity from O(N) to O(N/s2) (s: sub-sampling ratio) To verify the effectiveness of the algorithm, F-GDGIF is applied to cost aggregation and disparity refinement in stereo matching algorithms respectively. Experiments on the Middlebury evaluation benchmark demonstrate that F-GDGIF based stereo matching method can generate more accuracy disparity maps with low computational cost compared to other GIF based methods.

Improving DCP Haze Removal Scheme by Parameter Setting and Adaptive Gamma Correction

Recently, single-image haze removal based on the dark channel prior (DCP), originally proposed by He et. al., has attracted much attention in the image restoration community. This dehazing algorithm, called the DCP scheme here, is well-known to have four main problems in its dehazed images: artifacts, hue distortion, color over-saturation, and halos. In this paper, an improved DCP (IDCP) is proposed to deal with the four aforementioned problems, by setting the model parameters, i.e. scaling factors and window size and smoothing factor of a guided image filter in the DCP scheme. Note that a dehazed image is generally dim and low in contrast. An adaptive gamma correction (AGC) is introduced for dehazed image enhancement. The proposed IDCP and AGC are used to create the IDCP/AGC scheme, in which the IDCP scheme performs haze removal and the AGC enhances the dehazed image. The IDCP/AGC scheme was justified through extensive experiments and compared with the DCP scheme, an optimization-based scheme, and two learning-based schemes on two datasets. The results indicated that the proposed scheme is subjectively and objectively superior to the comparison schemes.

A new technique for guided filter based image denoising using modified cuckoo search optimization

In this work, a novel and efficient approach for image denoising is proposed. More often, noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image irrespective of their intensity values. This behaviour of noise can also be identified as Additive White Gaussian Noise (AWGN). For restoration of AWGN affected images, the proposed denoising approach is inspired by image adaptive guided image filtering using modified cuckoo search algorithm. The guidance image is itself derived from the noisy image for this purpose. Bilateral filtering smoothed noisy image is sharpened by unsharp masking and then employed as guidance image for the proposed optimal guided filtering approach. Optimal evaluation of parameters like guided filter smoothing parameter (regularization parameter or degree of smoothing (DoS)) and guided filter’s neighbourhood (kernel) size is done appropriately with the help of the modified cuckoo search algorithm. Two-dimensional search space is explored and exploited for deciding the behaviour of guided filtering adaptively as per the input image requirements. This guided image filter has a better behaviour at it acts as an edge preserving smoothing operator. It is considerably effective as its computational complexity is independent of filtering kernel size. A novel attempt is made by incorporating the Markov Random Field based Energy Minimization based objective/fitness function for imparting adaptive image denoising using metaheuristic intelligence. The proposed method is tested in terms of the performance metrics like peak signal to noise ratio, structural similarity index and mean square error. Performance of the proposed approach is compared with the already proposed image denoising techniques. For this comparison, only those methods are considered which were proposed for filtering of Gaussian Noise. Qualitative (visual) as well as quantitative (objective) results underlines the efficacy of the proposed method for filtering of Gaussian Noise.

Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising

Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the noise sensitivity of Curvelet phases — in contrast to their magnitude — reduces with higher noise level. Thus, we preserved the phase of the coefficients below threshold at coarser scale and estimated the corresponding magnitude by Joint Bilateral Filtering (JBF) technique. In contrast to the traditional hard thresholding, the coefficients in the finest scale is estimated using Bilateral Filtering (BF). The proposed filtering approach in the finest scale exhibits better connectedness among the edges, while removing the granular artifacts in the denoised image due to hard thresholding. Finally, the use of Guided Image Filter (GIF) on the Curvelet-based reconstructed image (initial denoised image in spatial domain) ensures the preservation of small image information with sharper edges and textures detail in the final denoised image. The lower noise sensitivity of Curvelet phase at higher noise strength accelerates the performance of proposed method over several state-of-the-art techniques and provides comparable outcome at lower noise levels.

Window‐aware guided image filtering via local entropy

Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over-smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window-aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non-box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity-similar strategy, the local non-box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non-box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.

A New Hybrid Stereo Disparity Estimation Algorithm with Guided Image Filtering-Based Cost Aggregation

Stereo matching algorithms are useful for estimating a dense depth characteristic of a scene by finding corresponding points from stereo images of the scene. Several factors such as occlusion, noise, and illumination inconsistencies in the scene affect the disparity estimates and make this process challenging. Algorithms developed to overcome these challenges can be broadly categorized as learning-based and non-learning based disparity estimation algorithms. The learning-based approaches are more accurate but computationally expensive. In contrary, non-learning based algorithms are widely used and are computationally efficient algorithms. In this paper, we propose a new stereo matching algorithm using guided image filtering (GIF)-based cost aggregation. The main contribution of our approach is a cost calculation framework which is a hybrid of cross-correlation between stereo-image pairs and scene segmentation (HCS). The performance of our HCS technique was compared with state-ofthe- art techniques using version 3 of the benchmark Middlebury dataset. Our results confirm the effective performance of the HCS technique.

Guided image filtering based ℓ0 gradient minimization for limited-angle CT image reconstruction

Abstract Limited-angle computed tomography (CT) reconstruction problem arises in some practical applications due to restrictions in the scanning environment or CT imaging device. Some artifacts will be presented in image reconstructed by conventional analytical algorithms. Although some regularization strategies have been proposed to suppress the artifacts, such as total variation (TV) minimization, there is still distortion in some edge portions of image. Guided image filtering (GIF) has the advantage of smoothing the image as well as preserving the edge. To further improve the image quality and protect the edge of image, we propose a coupling method, that combines ℓ 0 {\ell_{0}} gradient minimization and GIF. An intermediate result obtained by ℓ 0 {\ell_{0}} gradient minimization is regarded as a guidance image of GIF, then GIF is used to filter the result reconstructed by simultaneous algebraic reconstruction technique (SART) with nonnegative constraint. It should be stressed that the guidance image is dynamically updated as the iteration process, which can transfer the edge to the filtered image. Some simulation and real data experiments are used to evaluate the proposed method. Experimental results show that our method owns some advantages in suppressing the artifacts of limited angle CT and in preserving the edge of image.

Dual-Features Student-t Distribution Mixture Model Based Remote Sensing Image Registration

In the work, we present a novel multiviewpoint and multitemporal remote sensing image registration method based on a dual-features Student- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution mixture model (DSMM) under a variational Bayesian (VB) framework. The main contributions of the work are: 1) guided image filter (GIF) is adopted to smooth edges and strengthen ridges of images for heightening characteristics of feature point; 2) a Student- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution mixture model (SMM) based DSMM designs a global and local descriptor to estimate correspondences from local to global scale; and 3) local structure constraints are designed to preserve relationships of neighbors of points and the scale of neighborhood structure of points to constrain transformation. The experimental results demonstrate the better performance of our DSMM against five state-of-the-art methods.

Unsharp Mask Guided Filtering.

The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering by means of an additional guidance image. Where classical guided filters transfer structures using hand-designed functions, recent guided filters have been considerably advanced through parametric learning of deep networks. The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter. In this work, we posit that simultaneously estimating both coefficients is suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired by unsharp masking, a classical technique for edge enhancement that requires only a single coefficient, we propose a new and simplified formulation of the guided filter. Our formulation enjoys a filtering prior from a low-pass filter and enables explicit structure transfer by estimating a single coefficient. Based on our proposed formulation, we introduce a successive guided filtering network, which provides multiple filtering results from a single network, allowing for a trade-off between accuracy and efficiency. Extensive ablations, comparisons and analysis show the effectiveness and efficiency of our formulation and network, resulting in state-of-the-art results across filtering tasks like upsampling, denoising, and cross-modality filtering. Code is available at https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering.

Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids.

Model-based single image dehazing was widely studied due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model-based single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine structure in the image. A multi-scale dehazing algorithm is then proposed to address the latter problem by adopting Laplacian and Gaussian pyramids to decompose the hazy image into different levels and applying different haze removal and noise reduction approaches to restore the scene radiance at the different levels. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art dehazing algorithms.