Rolling Guidance Filter Research Articles

Systematising clustering techniques through cross-disciplinary research, leading to the development of new methods

Clustering algorithms can help scientists gain valuable insights from data. Thereâ–™s a variety of clustering methods in use, which means there are gaps between the methods used in different fields. Associate Professor Kohei Inoue, Department of Media Design, Kyushu University, Japan, wants to bridge these gaps by investigating the relationships among various clustering methods developed in different fields, in order to systematise the world of clustering. He is bringing two decades of research activities in pattern recognition and image processing to this work. In order to clarify the relationships between different clustering methods, Inoue and the team are conducting an interdisciplinary survey. First, the researchers are working to clarify the relationship between the technologies used across different fields. So far, they have successfully clarified the relationship between the rolling guidance filter and the local mode filter. In a previous study, Inoue and his collaborators proposed a robust K-means clustering al-algorithm. The researchers demonstrated the effectiveness of their technique utilising a BBC dataset originating from BBC News. In their work, the team is collaborating with a laboratory at a university in Japan that is studying non-photorealistic rendering. They have so far published several co-authored papers, as well as having obtained results from their joint research. Ultimately, by systemising clustering technology, Inoue believes that the characteristics of each method, as well as the interrelationships between each method, can be explained and clustering technology enhanced, as well as new clustering techniques developed.

Unsharp Masking of Medical Images using Rolling Guidance Filter

Medical Images consists of both high frequency and low frequency information needed by Physician for diagnostics and treatment purpose. Since, human eye is sensitive to high frequency information, the enhancement of high frequency information of medical image is very important. Most image enhancement techniques use the traditional unsharp mask filter to enhance the detail and edge information of medical image. While enhancing the detail and edge information, Gaussian Filter in traditional Unsharp Masking Filter introduces halo effects around edges. To remove this halo effects in enhanced images, this paper proposes a novel image enhancement filter in which Gaussian Filter is replaced by rolling guidance filter. This method provides satisfying results over Traditional Unsharp Mask Filter, Amended Unsharp Mask Filter, Anisotropic Diffusion based Unsharp Mask Filter and Guided Filtering based Unsharp Mask Filter in terms of high frequency information measures like mean gradient, spatial frequency and sharpness.

Automated extraction of blood vessels in retinal images using rolling guidance filter

Automated extraction of blood vessels in retinal images using rolling guidance filter

Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE.

Infrared sensors capture thermal radiation emitted by objects. They can operate in all weather conditions and are thus employed in fields such as military surveillance, autonomous driving, and medical diagnostics. However, infrared imagery poses challenges such as low contrast and indistinct textures due to the long wavelength of infrared radiation and susceptibility to interference. In addition, complex enhancement algorithms make real-time processing challenging. To address these problems and improve visual quality, in this paper, we propose a multi-scale FPGA-based method for real-time enhancement of infrared images by using rolling guidance filter (RGF) and contrast-limited adaptive histogram equalization (CLAHE). Specifically, the original image is first decomposed into various scales of detail layers and a base layer using RGF. Secondly, we fuse detail layers of diverse scales, then enhance the detail information by using gain coefficients and employ CLAHE to improve the contrast of the base layer. Thirdly, we fuse the detail layers and base layer to obtain the image with global details of the input image. Finally, the proposed algorithm is implemented on an FPGA using advanced high-level synthesis tools. Comprehensive testing of our proposed method on the AXU15EG board demonstrates its effectiveness in significantly improving image contrast and enhancing detail information. At the same time, real-time enhancement at a speed of 147 FPS is achieved for infrared images with a resolution of 640 × 480.

A Spectral Enhancement Method Based on Remote-Sensing Images for High-Speed Railways

This paper proposes a pansharpening model in order to obtain remote-sensing images with high spatial resolution and high spectral resolution. Based on a generic component substitution (CS) fusion framework, the model utilizes the difference between the high-frequency component of the panchromatic (PAN) image and the high-frequency component of the luminance (L) image to express the missing spatial detail information of the ideal high-resolution multispectral (HRMS) image. A rolling guidance filter (RGF) is used in this framework to achieve the effective extraction of high-frequency information from remote-sensing images while reducing the spectral distortion of subsequent operations. The modulation transfer function (MTF) values of the sensor are also applied to the selection of adaptive weighting coefficients to further improve the spectral fidelity of the fused images. At the same time, the choice of suitable interpolation and gain coefficients improves the generalizability of the model while reducing spectral and spatial distortions. Finally, the use of a guided filter (GF) also greatly improves the quality of the fused image. The experimental results show that the model can effectively improve the spatial resolution for foreign objects at the perimeter of high-speed railways, while also ensuring the color fidelity of foreign objects such as colored steel tiles.

A novel fusion method for infrared and visible images under poor illumination conditions

Most infrared and visible image fusion methods are designed based on the fact that visible images have rich scene information and more details like edges and textures than infrared images, while infrared images have prominent thermal target information. However, under poor illumination conditions, most areas of visible images are dark, may contain much noise, and lack the corresponding detail information compared with infrared images. As a result, fused images of those methods suffer from information loss, low contrast, and non-obvious targets. To solve this problem, we propose a novel fusion method. Firstly, an improved rolling guidance filter, named RF-RGF, is proposed to decompose source images into small-scale detail, large-scale detail, and base layers. Secondly, for the fusion of small-scale detail layers, a new nonlinear function-based rule is proposed to transfer more texture information from source images under poor illumination conditions to the fused images. For the fusion of large-scale detail layers, a novel fusion rule based on the weighted sum of support values (WSSV) is constructed to retain details effectively. Then, for the fusion of base layers, the rule based on the visual saliency map (VSM) is adopted to ensure high contrast and a well overall look of the fused image. Moreover, BIMEF and morphological bright and dark details (MBD) are used to further enhance the fused image’s contrast and details, making targets more obvious. Specifically, BIMEF is adopted to enhance the visible image before decomposition. The MBD obtained by two selective rules based on morphological top- and bottom-transformations (MTB) is used to enhance the base layer. Experimental results show that the fusion performance of the proposed method is better than other methods (including some state-of-the-art methods), especially in artifact suppression, information retention, contrast improvement, and target enhancement.

A Novel Saliency-Based Decomposition Strategy for Infrared and Visible Image Fusion

The image decomposition strategy that extracts salient features from the source image is crucial for image fusion. To this end, we proposed a novel saliency-based decomposition strategy for infrared and visible image fusion. In particular, the latent low-rank representation (LatLRR) and rolling guidance filter (RGF) are together employed to process source images, which is called DLatLRR_RGF. In this method, the source images are first decomposed to salient components and base components based on LatLRR, and the salient components are filtered by RGF. Then, the final base components can be calculated by the difference between the source image and the processed salient components. The fusion rule based on the nuclear-norm and modified spatial frequency is used to fuse the salient components. The base components are fused by the l2-energy minimization model. Finally, the fused image can be obtained by the fused base components and saliency detail components. Multiple groups of experiments on different pairs of infrared and visible images demonstrate that, compared with other state-of-the-art fusion algorithms, our proposed method possesses superior fusion performance from subjective and objective perspectives.

An Effective Infrared and Visible Image Fusion Approach via Rolling Guidance Filtering and Gradient Saliency Map

To solve problems of brightness and detail information loss in infrared and visible image fusion, an effective infrared and visible image fusion method using rolling guidance filtering and gradient saliency map is proposed in this paper. The rolling guidance filtering is used to decompose the input images into approximate layers and residual layers; the energy attribute fusion model is used to fuse the approximate layers; the gradient saliency map is introduced and the corresponding weight matrices are constructed to perform on residual layers. The fusion image is generated by reconstructing the fused approximate layer sub-image and residual layer sub-images. Experimental results demonstrate the superiority of the proposed infrared and visible image fusion method.

An illumination-dependent adaptive fusion method for infrared and visible images

Changes in illumination conditions of the imaging scene cause changes in the relative amount of complementary information between the infrared and visible images. Therefore, the fusion method should adjust to changing illumination conditions to adaptively fuse as much source images’ information as possible. However, most existing fusion methods do not consider the changes in the illumination conditions in the construction process, resulting in the loss of detail information and low contrast in fused images. We propose an illumination-dependent adaptive fusion (IDA Fusion) method to solve this problem. First, to process detail information of different scales and brightness information separately, the multi-scale rolling guidance filter (RGF) is chosen to decompose source images into small-scale detail layers, large-scale detail layers, and base layers. Second, according to the respective characteristics of small-scale and large-scale details under different illumination conditions, two different illumination-dependent rules are designed to combine the small-scale and large-scale detail layers, respectively. These rules adjust their forms and parameters according to the average pixel value and entropy ratio to adaptively transfer the detail information from source to fused images. Moreover, for combining the base layers, a rule based on weighted least squares (WLS) minimization is proposed to keep as much source images’ information as possible while maintaining an appropriate brightness under different illumination conditions. Experimental results validate the effectiveness of the above rules and demonstrate that our method performs better than some state-of-the-art methods, including information retention and contrast improvement.

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)

Infrared and Visible Image Fusion Based on Image Enhancement and Rolling Guidance Filtering

Infrared and Visible Image Fusion Based on Image Enhancement and Rolling Guidance Filtering

Multimodal Brain Image Fusion Based on Improved Rolling Guidance Filter and Wiener Filter.

Medical image fusion technology can integrate complementary information from different modality medical images to provide a more complete and accurate description of the specific diagnosed object, which is very helpful for image-guided clinical diagnosis and treatment. This paper proposes an effective brain image fusion framework based on improved rolling guidance filter (IRGF). Firstly, input images are decomposed into base layers and detail layers using the IRGF and Wiener filter. Secondly, the visual saliency maps of the input image are computed by pixel-level saliency value, and the weight maps of detail layers are constructed by max-absolute strategy and are further smoothed with Gaussian filter, the purpose of which is to make the fused image appear more naturally and more suitable for human visual perception. Lastly, base layers are fused by visual saliency map based fusion rule and the corresponding weight maps from detail layers are fused by the weighted least squares optimization scheme. Experimental results testify that our method is superior to some state-of-the-art methods in both subjective and objective assessments.

Tri-modal medical image fusion based on adaptive energy choosing scheme and sparse representation

Multimodal medical image fusion integrates useful information from multiple single-modal medical images, generating a more comprehensive and objective fused image that better assist clinical applications. In this paper, a novel tri-modal medical image fusion method based on cartoon-texture decomposition is proposed and performed using a rolling guidance filter, and sparse representation, to fuse the texture components. Furthermore, a novel adaptive energy choosing scheme is proposed to fuse the cartoon components; through this approach, the brightness of cartoon components can be effectively detected. Finally, the fused image is reconstructed by combining the fused texture and cartoon components. Experimental results demonstrate that the proposed method yields better performance than some state-of-the-art methods in subjective and objective assessments. Meanwhile, the average level of the proposed method are 28.44%, 8.94%, 0.07%, 16.09%, 58.66%, and 0.34% higher than the compared methods evaluated by the metrics including QMI, QTE, QNCIE, QG, QP and EN, respectively.

SAR‐IRGF: A novel improved rolling guidance filter for the synthetic aperture radar image

Inspired by the framework of the rolling guidance filter (RGF), a novel improved RGF for synthetic aperture radar (SAR) images is proposed in this letter, named SAR-IRGF. Based on the model of additive noise, the RGF has the complete control of detailed smoothing under a scale measure for optical images. However, the speckle is well known as a multiplicative noise, which invalidates the RGF for SAR images. In this letter, an appropriate edge guidance is utilized to optimize the RGF for SAR image denoising. Compared with the original version, better convergence is achieved. Experimental results on natural Ku-band airborne SAR images show that the proposed SAR-IRGF reduces more speckle while sharpening the edges effectively. Simultaneously, the SAR-IRGF demonstrates a significant nearly 5.5-fold improvement than RGF in the equivalent number of looks (ENL).

An Image Fusion Algorithm Based on Improved RGF and Visual Saliency Map.

To solve the artifact problem in fused images and the lack of enough generalization under different scenarios of existing fusion algorithms, the paper proposes an image fusion algorithm based on improved RGF and visual saliency map to realize fusion for infrared and visible light images and a multimode medical image. Firstly, the paper uses RGF (rolling guidance filter) and Gaussian filter to decompose the image into the base layer, interlayer, and detail layer by a different scale. Secondly, the paper obtains a visual weight map by the calculation of the source image and uses the guided filter to better guide the base layer fusion. Then, it realizes the interlayer fusion through maximum local variance and realizes the detail layer fusion through the maximum absolute value of the pixel. Finally, it obtains the fused image through weight fusion. The experiment demonstrates that the proposed method shows better comprehensive performance and obtains better results in fusion for infrared and visible light images and medical images compared to the contrast method.

Infrared and Visible Image Fusion Based on Visual Saliency Map and Image Contrast Enhancement.

The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy to produce artifacts, and the information of the visible target and texture details are not fully preserved, especially for the image fusion under dark scenes and smoke conditions. Therefore, an infrared and visible image fusion method is proposed based on visual saliency image and image contrast enhancement processing. Aiming at the problem that low image contrast brings difficulty to fusion, an improved gamma correction and local mean method is used to enhance the input image contrast. To suppress artifacts that are prone to occur in the process of image fusion, a differential rolling guidance filter (DRGF) method is adopted to decompose the input image into the basic layer and the detail layer. Compared with the traditional multi-scale decomposition method, this method can retain specific edge information and reduce the occurrence of artifacts. In order to solve the problem that the salient object of the fused image is not prominent and the texture detail information is not fully preserved, the salient map extraction method is used to extract the infrared image salient map to guide the fusion image target weight, and on the other hand, it is used to control the fusion weight of the basic layer to improve the shortcomings of the traditional ‘average’ fusion method to weaken the contrast information. In addition, a method based on pixel intensity and gradient is proposed to fuse the detail layer and retain the edge and detail information to the greatest extent. Experimental results show that the proposed method is superior to other fusion algorithms in both subjective and objective aspects.

Multi-modality image fusion for medical assistive technology management based on hybrid domain filtering

Medical image fusion technique combines images from different imaging modalities to enhance the reliability and accuracy of the medical diagnoses that play an increasingly significant role in clinical applications. In the domain of medical image fusion, it has made huge advancement in the span of past few years. However medical image fusion stills remain a challenging task since the recent methods still suffers from colour distortion, blurring and noise in their fusion results. In order to address the shortcomings of recent methods, this paper presents a novel method to fuse multimodal medical images by employing hybridization of cross bilateral filter and edge aware filtering using pixel-based fusion rule. This method proposed a novel algorithm for computation of weights for final fusion rule. First, a cross bilateral filter (CBF) is employed to both source images which considers both geometric closeness and gray level of neighbourhood pixels by avoiding smoothing the edges and utilizes one image to find the kernel and another to filter and vice versa. Detail images are obtained after subtraction of CBF output from the corresponding source images. With the help of domain transform filter, small scale features of the detail images are retrieved from the surrounding of large-scale structures of these images. These detail images are further processed via rolling guidance filter (RGF) which eliminates small-scale structures and refine edges to preserve large-scale structures of detail images. The weights are computed while measuring the strength of details in the detail images. The computed weights are then directly multiplied with source images followed by weight normalization in order to obtain final fusion result. Qualitative and quantitative experiments are performed on the publicly available datasets, where the results demonstrate that the proposed method outperforms the state-of-the-art, in terms of both visual effect and objective evaluation metrics. Our method generates clear and clean fused images which do not suffers from noise and artifacts while preserving essential information of the multimodal images.

Adaptive infrared and visible image fusion method by using rolling guidance filter and saliency detection

Infrared and visible images have good complementarity; thus, infrared and visible image fusion methods are extensively used in military, target detection, pattern recognition, and other applications. However, problems such as halo effect, poor background texture, and contrast reduction are encountered in image fusion methods. In addition, when the visible image is disturbed by smoke and high brightness, it will affect the quality of the fused image, making image fusion a challenge. To address these problems, an adaptive image fusion method is proposed in this paper. First, the image decomposition method based on rolling guidance filter and saliency detection (RGFSD) is proposed. Next, the source image is decomposed into three layers by using RGFSD: detail layer, salient layer, and base layer. The detail layer is then fused using the phase congruency strategy. Subsequently, the local entropy and gradient (LEG) method is proposed to simulate the perception of significant information by the human visual system to assign the weights for salient layer fusion. Next, the base layer is fused using the averaging method. Furthermore, an image enhancement method based on rolling guidance filter and contrast-limited adaptive histogram equalization is proposed to enhance the detail and contrast of the fused image. Finally, the proposed method is compared qualitatively and quantitatively with eight fusion methods. The experiment results show that compared with the existing methods, the proposed method can better enhance the contrast and retain details of the source images.

Infrared and visible image fusion via multi-scale multi-layer rolling guidance filter

Infrared and visible image fusion via multi-scale multi-layer rolling guidance filter

Underwater image enhancement by color correction and color constancy via Retinex for detail preserving

In underwater, light attenuation causes non-uniform illumination that degrades underwater image. To enhance the degraded image, we propose an underwater image enhancement method that includes color correction, color constancy, multi-scale retinex (MSR), edge-aware and texture smoothing filters, and contrast stretching. The formulated color correction compensates the red and blue channels by masking. Color constancy transforms non-uniform illumination into uniform illumination. Furthermore, the gamma correction in the color constancy improves the contrast of the image. The distorted colors are restored using the MSR with contrast limited adaptive histogram equalization (CLAHE). The edges are preserved using a fast local laplacian filter (FLLF), while the detailed texture of the image is smoothed using a rolling guidance filter (RGF) The contrast stretching enhances the image by stretching the pixel values. The qualitative and quantitative evaluation of the proposed algorithm outperforms the existing methods in terms of naturalness, contrast, visibility and enhancement of low-light images.