Multi-scale Edge Enhancement Research Articles

A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .

MEEAFusion: Multi-Scale Edge Enhancement and Joint Attention Mechanism Based Infrared and Visible Image Fusion.

Infrared and visible image fusion can integrate rich edge details and salient infrared targets, resulting in high-quality images suitable for advanced tasks. However, most available algorithms struggle to fully extract detailed features and overlook the interaction of complementary features across different modal images during the feature fusion process. To address this gap, this study presents a novel fusion method based on multi-scale edge enhancement and a joint attention mechanism (MEEAFusion). Initially, convolution kernels of varying scales were utilized to obtain shallow features with multiple receptive fields unique to the source image. Subsequently, a multi-scale gradient residual block (MGRB) was developed to capture the high-level semantic information and low-level edge texture information of the image, enhancing the representation of fine-grained features. Then, the complementary feature between infrared and visible images was defined, and a cross-transfer attention fusion block (CAFB) was devised with joint spatial attention and channel attention to refine the critical supplemental information. This allowed the network to obtain fused features that were rich in both common and complementary information, thus realizing feature interaction and pre-fusion. Lastly, the features were reconstructed to obtain the fused image. Extensive experiments on three benchmark datasets demonstrated that the MEEAFusion proposed in this research has considerable strengths in terms of rich texture details, significant infrared targets, and distinct edge contours, and it achieves superior fusion performance.

Highly efficient encoder-decoder network based on multi-scale edge enhancement and dilated convolution for LDCT image denoising

Highly efficient encoder-decoder network based on multi-scale edge enhancement and dilated convolution for LDCT image denoising

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.

结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割

结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割

Curvelet Transform based Enhancements on Retinal Blood Vessel Segmentation Compared with Contourlet Transform

Retinal images are used in several applications, such as ocular fundus operations as well as human recognition. Also, they play important roles in detection of certain diseases in early stages, such as diabetic retinopathy, hypertensive retinopathy, which can be performed by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Due to the acquisition process, retinal images often have low gray level contrast and dynamic range. This problem may seriously affect the diagnostic procedure and its results. Several techniques have been used to enhance the image quality. In this work, a new multi-scale method for retinal image contrast enhancement based on the Contourlet transform is presented. The Contourlet transform has better performance in representing edges than wavelets and curvelets, and is therefore well-suited for multi-scale edge enhancement. Contourlet coefficients in corresponding subbands are modified via a nonlinear function for better visualization. Application of this method on images show that the proposed approach outperforms the Curvelet transform, with an encouraging improvement in representing the thin vessels, and might be helpful for vessel segmentation. The segmentation of blood vessels from the enhanced image is performed based on mathematical morphological transform. The morphological operators are used to smoothen the background, allowing vessels, to be seen clearly and to eliminate the non-vessel pixels. The proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

A Hybrid Fast WOC (wavelet Otsu curvelet) Algorithm for Stem Cell Image Segmentation

This paper mainly concentrates on image segmentation using Wavelet, otsu and Curvelet algorithm. The existing Chan Vese model becomes complex in determining multiple images simultaneously in varying intensities. In order to increase the detection performance the WOC (wavelet Otsu curvelet) algorithm is proposed. Due to high directionality and anisotropic nature of the curvelet transform, it gives better performance at the edges and it is also applied for multi-scale edge enhancement. Wavelet transform is well suited for multi resolution. Wavelet and curvelet transforms are incorporated for sub band decomposition of frequency coefficients. Otsu algorithm has a novel approach for segmentation where thresholding is done using histogram analysis. This in turn reduces the segmentation complexity and hence the new algorithm is termed as Hybrid fast WOC algorithm.

Enhancing retinal image by the Contourlet transform

The evaluation of retinal images is widely used to help doctors diagnose many diseases, such as diabetes or hypertension. Due to the acquisition process, retinal images often have low grey level contrast and dynamic range. This problem may seriously affect the diagnostic procedure and its results. Here we present a new multi-scale method for retinal image contrast enhancement based on the Contourlet transform. The Contourlet transform has better performance in representing edges than wavelets for its anisotropy and directionality, and is therefore well-suited for multi-scale edge enhancement. We modify the Contourlet coefficients in corresponding subbands via a nonlinear function and take the noise into account for more precise reconstruction and better visualization. We compare this approach with enhancement based on the Wavelet transform, Histogram Equalization, Local Normalization and Linear Unsharp Masking. The application of this method on images from the DRIVE database showed that the proposed approach outperforms other enhancement methods on low contrast and dynamic range images, with an encouraging improvement, and might be helpful for vessel segmentation.

Contrast enhancement in retinal image via multi-scale geometrical analysis

We present a new multi-scale geometrical analysis method for ophthalmic image contrast enhancement based on the contourlet transform. The contourlet transform has better performance in representing edges than wavelets due to its anisotropy and directionality, and is therefore well-suited for multiscale edge enhancement. We modify the contourlet coefficients in corresponding subband and take the noise into account for more precise reconstruction and better visualization. We compare this approach with enhancement based on the curvelet transform, and the traditional Histogram Stretching (HS). Our findings are that contourlet based enhancement out-performs other enhancement methods on low contrast and dynamic range images, and can clearly identify the vessels and nerves in an ophthalmic image.

Gray and color image contrast enhancement by the curvelet transform

We present in this paper a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge detection and segmentation, among other processing applications, to provide for quantitative comparative evaluation. Our findings are that curvelet based enhancement out-performs other enhancement methods on noisy images, but on noiseless or near noiseless images curvelet based enhancement is not remarkably better than wavelet based enhancement.