scholarly journals Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method

2021 ◽  
Author(s):  
Muhammad Adeel Azam ◽  
Khan Bahadar Khan ◽  
Eid Rehman ◽  
Sana Ullah Khan
Keyword(s):  
Image Contrast ◽  
Exposure Level ◽  
Local Contrast ◽  
Surgical Smoke ◽  
Exposure Fusion ◽  
Illumination System ◽  
Fused Image ◽  
Adaptive Structure ◽  
Blurred Image ◽  
Structure Decomposition

Abstract In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion (PASD-MEF) technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images are obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition

2020 ◽  
Vol 20 (14) ◽  
pp. 8062-8072 ◽  
Author(s):  
Mingyao Zheng ◽  
Guanqiu Qi ◽  
Zhiqin Zhu ◽  
Yuanyuan Li ◽  
Hongyan Wei ◽  
...  
Keyword(s):  
Image Fusion ◽  
Fusion Method ◽  
Image Dehazing ◽  
Adaptive Structure ◽  
Structure Decomposition ◽  
Artificial Image ◽  
Image Fusion Method

scholarly journals Multimodal Medical Image Fusion by Adaptive Manifold Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Geng ◽  
Shuaiqi Liu ◽  
Shanna Zhuang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Low Frequency ◽  
Filter Method ◽  
Local Contrast ◽  
Complex Wavelet Transform ◽  
Image Guided Radiotherapy ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Fusion Methods

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

scholarly journals A Novel Approach for Detail-Enhanced Exposure Fusion Using Guided Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Harbinder Singh ◽  
Vinay Kumar ◽  
Sunil Bhooshan
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Guided Filter ◽  
Base Layer ◽  
Range Image ◽  
Exposure Fusion ◽  
Novel Approach ◽  
Fused Image ◽  
High Dynamic ◽  
Fusion Approach

In this paper we propose a novel detail-enhancing exposure fusion approach using nonlinear translation-variant filter (NTF). With the captured Standard Dynamic Range (SDR) images under different exposure settings, first the fine details are extracted based on guided filter. Next, the base layers (i.e., images obtained from NTF) across all input images are fused using multiresolution pyramid. Exposure, contrast, and saturation measures are considered to generate a mask that guides the fusion process of the base layers. Finally, the fused base layer is combined with the extracted fine details to obtain detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions without High Dynamic Range Image (HDRI) representation and tone mapping step. Moreover, we have demonstrated that the proposed method is also suitable for the multifocus image fusion without introducing artifacts.

scholarly journals Visible and Near-Infrared Image Acquisition and Fusion for Night Surveillance

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 75
Author(s):  
Hyuk-Ju Kwon ◽  
Sung-Hak Lee
Keyword(s):  
Image Fusion ◽  
Near Infrared ◽  
Color Space ◽  
Infrared Image ◽  
Human Vision ◽  
Surveillance Systems ◽  
Local Contrast ◽  
Visible Image ◽  
Exposure Fusion ◽  
Multiple Exposures

Image fusion combines images with different information to create a single, information-rich image. The process may either involve synthesizing images using multiple exposures of the same scene, such as exposure fusion, or synthesizing images of different wavelength bands, such as visible and near-infrared (NIR) image fusion. NIR images are frequently used in surveillance systems because they are beyond the narrow perceptual range of human vision. In this paper, we propose an infrared image fusion method that combines high and low intensities for use in surveillance systems under low-light conditions. The proposed method utilizes a depth-weighted radiance map based on intensities and details to enhance local contrast and reduce noise and color distortion. The proposed method involves luminance blending, local tone mapping, and color scaling and correction. Each of these stages is processed in the LAB color space to preserve the color attributes of a visible image. The results confirm that the proposed method outperforms conventional methods.

scholarly journals Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3610
Author(s):  
Haonan Su ◽  
Cheolkon Jung ◽  
Long Yu
Keyword(s):  
Near Infrared ◽  
Local Contrast ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Infrared Images ◽  
Multi Scale ◽  
Discrepancy Model ◽  
Fused Image ◽  
Contrast Preservation ◽  
Posteriori Estimation

We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.

A Novel Image Fusion Algorithm Based on Local Contrast and Improved PCNN Model

2010 ◽  
Vol 108-111 ◽  
pp. 21-26
Author(s):  
Ming Ye
Keyword(s):  
Vision System ◽  
Human Vision ◽  
Local Contrast ◽  
Slight Effect ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Traditional Algorithm ◽  
Fused Image ◽  
Selection Operator ◽  
Traditional Approaches

The proposed new fusion algorithm is based on the improved pulse coupled neural network (PCNN) model, the fundamental characteristics of images and the properties of human vision system. Compared with the traditional algorithm where the linking strength of each neuron is the same and its value is chosen through experimentation, this algorithm uses the contrast of each pixel as its value, so that the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image taking part in the fusion. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. Furthermore, by this algorithm, other parameters, for example, , the threshold adjusting constant, only have a slight effect on the new fused image. Therefore, it overcomes the difficulty in adjusting parameters in PCNN. Experimental results indicate that the method outperforms the traditional approaches in preserving edge information while improving texture information.

scholarly journals Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

2021 ◽  
Vol 12 (5) ◽  
pp. 1559-1568
Author(s):  
Gutta Srinivas Rao, Et. al.
Keyword(s):  
Contrast Enhancement ◽  
Reconstruction Algorithm ◽  
Time Frame ◽  
Image Contrast ◽  
Reconstruction Method ◽  
Morphological Operations ◽  
Local Contrast ◽  
Morphological Transformation ◽  
Structural Element ◽  
Image Contrast Enhancement

The paper presents a novel algorithm for the computation of the image decomposition using a morphological filter with reconstruction. The target applications are image contrast enhancement especially those with high dynamic content. Both bright and dark regions contrast enhancement were considered. A new hardware efficient implementation of decomposition is presented. Following decomposition in 5 levels of detail a local contrast enhancement is performed. The new reconstruction algorithm and its hardware implementation as proposed is shown to be independent on structural element size and that it results in a predictable time frame operation. A mixed schematic and VHDL/Verilog description of the decomposition filters was synthesized and results show far higher speed performance compared with solutions found in recent literature. Image organizing strategies are essentially required to the pictures captured by satellites. The grey levels in the image captured by satellites need to be normalized for improving picture contrast. The pictures captured by satellites are generally poor in quality and the contrast levels are also very less. In this manuscript a differentiation approach dependent on scientific morphology called rotational morphological transformations are proposed using Modified Opening by Reconstruction Method (MORM). This methodology upgrade pictures with poor contrast and unable to recognize the objects in the image. In the images with poor lightening, morphological operations are performed to improve its clarity and contrast. Picture enhancement has been done by applying morphological operations on the satellite images considered. The technique utilizes data from picture by squares, while the morphological methods change the strategy using the contrast enhancement activity, which is utilized to characterize the multi-foundation poor lightening pictures. The total picture handling procedure is implemented utilizing MATLAB reproduction model. The MORM method is compared with the traditional methods and the results show that the proposed method is better in improving the accuracy rate.

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