Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

Topic highlight on texture and color enhancement imaging in gastrointestinal diseases

Olympus Corporation developed texture and color enhancement imaging(TXI)as a novel image-enhancing endoscopic technique.This topic highlights a series of hot-topic articles that investigated the efficacy of TXI for gastrointestinal disease identification in the clinical setting.A randomized controlled trial demonstrated improvements in the colorectal adenoma detection rate(ADR)and the mean number of adenomas per procedure(MAP)of TXI compared with those of white-light imaging(WLI)observation(58.7%vs 42.7%,adjusted relative risk 1.35,95%CI:1.17-1.56;1.36 vs 0.89,adjusted incident risk ratio 1.48,95%CI:1.22-1.80,respectively).A cross-over study also showed that the colorectal MAP and ADR in TXI were higher than those in WLI(1.5 vs 1.0,adjusted odds ratio 1.4,95%CI:1.2-1.6;58.2%vs 46.8%,1.5,1.0-2.3,respectively).A randomized controlled trial demonstrated non-inferiority of TXI to narrow-band imaging in the colorectal mean number of adenomas and sessile serrated lesions per procedure(0.29 vs 0.30,difference for non-inferiority-0.01,95%CI:-0.10 to 0.08).A cohort study found that scoring for ulcerative colitis severity using TXI could predict relapse of ulcerative colitis.A cross-sectional study found that TXI improved the gastric cancer detection rate compared to WLI(0.71%vs 0.29%).A cross-sectional study revealed that the sensitivity and accuracy for active Helicobacter pylori gastritis in TXI were higher than those of WLI(69.2%vs 52.5%and 85.3%vs 78.7%,res-pectively).In conclusion,TXI can improve gastrointestinal lesion detection and qualitative diagnosis.Therefore,further studies on the efficacy of TXI in clinical practice are required.

Is it a normal phenomenon for pediatric patients to have brain leptomeningeal contrast enhancement on 3-tesla magnetic resonance imaging?

Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.

Enhancing the Quality of Low-Light Printed Circuit Board Images through Hue, Saturation, and Value Channel Processing and Improved Multi-Scale Retinex

To address the issue of deteriorated PCB image quality in the quality inspection process due to insufficient or uneven lighting, we proposed an image enhancement fusion algorithm based on different color spaces. Firstly, an improved MSRCR method was employed for brightness enhancement of the original image. Next, the color space of the original image was transformed from RGB to HSV, followed by processing the S-channel image using bilateral filtering and contrast stretching algorithms. The V-channel image was subjected to brightness enhancement using adaptive Gamma and CLAHE algorithms. Subsequently, the processed image was transformed back to the RGB color space from HSV. Finally, the images processed by the two algorithms were fused to create a new RGB image, and color restoration was performed on the fused image. Comparative experiments with other methods indicated that the contrast of the image was optimized, texture features were more abundantly preserved, brightness levels were significantly improved, and color distortion was prevented effectively, thus enhancing the quality of low-lit PCB images.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values.The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone.The brain Magnetic Resonance Imaging(MRI)scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer.Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease.To solve this issue,this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis.The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability.The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety ofMRIscan datasets of variable quality.ForMRI scans,the BRISQUE“blind/referenceless image spatial quality evaluator”and the NIQE“natural image quality evaluator”measures were 39.38 and 3.58,respectively.The proposed image enhancement model,according to the data,produces the best image quality ratings,and it may be able to aid medical experts in the diagnosis process.The experimental results were achieved using a publicly available collection of MRI scans.

A Text Image Watermarking Algorithm Based on Image Enhancement

Digital watermarking technology is adequate for copyright protection and content authentication.There needs to be more research on the watermarking algorithm after printing and scanning.Aiming at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is proposed.This algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the watermark.Experiments show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above 0.93.Especially,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method.The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline.Due to the differences in satellite sensors when producing images,subtle but inherent stripes can appear at the stitching positions between the sensors.These stitchingstripes cannot be eliminated by conventional relative radiometric calibration.The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation,classification and interpretation of remote sensing images.Therefore,a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper.First,the inconsistency in grayscales around stripes is eliminated with the statistical method.Second,the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality.Finally,the details of the images are highlighted by a new image enhancement method,which makes the whole image clearer.Comprehensive experiments are performed,and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training data.To address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms.This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images.In the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images.In the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image enhancement.Experiments show the effectiveness and rationality of each module designed in this paper.And the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.

Underwater Image Enhancement Based on IMSRCR and CLAHE-WGIF

Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed.Firstly,the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction;secondly,the image is converted to HSV color space,and the segmentation exponential algorithm is used to process the S component to enhance the image saturation;finally,multi-scale Retinex is used to decompose the V component image into detail layer and base layer,and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness,while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information.The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations,and the information entropy of the image is improved by 6.3%on average,and the UIQM and UCIQE indexes are improved by 12.9%and 20.3%on average.

Hyperspectral image super resolution using deep internal and self-supervised learning

By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)image.With previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth data.Thus,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in reality.The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse environments.To handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem.The authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS version.However,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction performance.To solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training samples.Specifically,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for measuring the network modelling performance.By consolidating the DIL and DSL into a unified deep framework,the authors construct a more robust HSI SR method without any prior training and have great potential of flexible adaptation to different settings per obser-vation.To verify the effectiveness of the proposed approach,extensive experiments have been conducted on two benchmark HS datasets,including the CAVE and Harvard datasets,and demonstrate the great performance gain of the proposed method over the state-of-the-art methods.

Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection

BACKGROUND Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma(HCC)has great significance for patient prognosis.AIM To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced magnetic resonance imaging(MRI)combined with clinical features in predicting early recurrence of HCC after resection.METHODS A total of 161 patients with pathologically confirmed HCC were enrolled.The patients were divided into early recurrence and non-early recurrence group based on the follow-up results.The clinical,laboratory,pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed.RESULTS Of 161 patients,73 had early recurrence and 88 were had non-early recurrence.Univariate analysis showed that patient age,gender,serum alpha-fetoprotein level,the Barcelona Clinic Liver Cancer stage,China liver cancer(CNLC)stage,microvascular invasion(MVI),pathological satellite focus,tumor size,tumor number,tumor boundary,tumor capsule,intratumoral necrosis,portal vein tumor thrombus,large vessel invasion,nonperipheral washout,peritumoral enhancement,hepatobiliary phase(HBP)/tumor signal intensity(SI)/peritumoral SI,HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation.Multivariate logistic regression analysis showed that patient age,MVI,CNLC stage,tumor boundary and large vessel invasion were independent predictive factors.External data validation indicated that the area under the curve of the combined predictors was 0.861,suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC.CONCLUSION Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

Improved visibility of colorectal tumor by texture and color enhancement imaging with indigo carmine

BACKGROUND Accurate diagnosis and early resection of colorectal polyps are important to prevent the occurrence of colorectal cancer.However,technical factors and morphological factors of polyps itself can lead to missed diagnoses.Imageenhanced endoscopy and chromoendoscopy(CE)have been developed to facilitate an accurate diagnosis.There have been no reports on visibility using a combination of texture and color enhancement imaging(TXI)and CE for colorectal tumors.AIM To investigate the visibility of margins and surfaces with the combination of TXI and CE for colorectal lesions.METHODS This retrospective study included patients who underwent lower gastrointestinal endoscopy at the Toyoshima Endoscopy Clinic.We extracted polyps that were resected and diagnosed as adenomas or serrated polyps(hyperplastic polyps and sessile serrated lesions)from our endoscopic database.An expert endoscopist performed the lower gastrointestinal endoscopies and observed the lesion using white light imaging(WLI),TXI,CE,and TXI+CE modalities.Indigo carmine dye was used for CE.Three expert endoscopists rated the visibility of the margin and surface patterns in four ranks,from 1 to 4.The primary outcomes were the average visibility scores for the margin and surface patterns based on the WLI,TXI,CE,and TXI+CE observations.Visibility scores between the four modalities were compared by the Kruskal-Wallis and Dunn tests.RESULTS A total of 48 patients with 81 polyps were assessed.The histological subtypes included 50 tubular adenomas,16 hyperplastic polyps,and 15 sessile serrated lesions.The visibility scores for the margins based on WLI,TXI,CE,and TXI+CE were 2.44±0.93,2.90±0.93,3.37±0.74,and 3.75±0.49,respectively.The visibility scores for the surface based on WLI,TXI,CE,and TXI+CE were 2.25±0.80,2.84±0.84,3.12±0.72,and 3.51±0.60,respectively.The visibility scores for the detection and surface on TXI were significantly lower than that on CE but higher than that on WLI(P<0.001).The visibility scores for the margin and surface on TXI+CE were significantly higher than those on CE(P<0.001).In the sub-analysis of adenomas,the visibility for the margin and surface on TXI+CE was significantly better than that on WLI,TXI,and CE(P<0.001).In the sub-analysis of serrated polyps,the visibility for the margin and surface on TXI+CE was also significantly better than that on WLI,TXI,and CE(P<0.001).CONCLUSION TXI+CE enhanced the visibility of the margin and surface compared to WLI,TXI,and CE for colorectal lesions.

Real-time and high-transmission middle-infrared optical imaging system based on a pixel-wise metasurface micro-polarization array

Real-time polarization medium-wave infrared(MIR)optical imaging systems enable the acquisition of infrared and polarization information for a target.At present,real-time polarization MIR devices face the following problems:poor real-time performance,low transmission and high requirements for fabrication and integration.Herein,we aim to improve the performance of real-time polarization imaging systems in the MIR waveband and solve the above-mentioned defects.Therefore,we propose a MIR polarization imaging system to achieve real-time polarization-modulated imaging with high transmission as well as improved performance based on a pixel-wise metasurface micro-polarization array(PMMPA).The PMMPA element comprises several linear polarization(LP)filters with different polarization angles.The optimization results demonstrate that the transmittance of the center field of view for the LP filters is up to 77%at a wavelength of4.0μm and an extinction ratio of 88 d B.In addition,a near-diffraction-limited real-time MIR imaging optical system is designed with a field of view of 5°and an F-number of 2.The simulation results show that an MIR polarization imaging system with excellent real-time performance and high transmission is achieved by using the optimized PMMPA element.Therefore,the method is compatible with the available optical system design technologies and provides a way to realize real-time polarization imaging in MIR wavebands.

Does sevoflurane sedation in pediatric patients lead to“pseudo”leptomeningeal enhancement in the brain on 3 Tesla magnetic resonance imaging?

BACKGROUND Prominent leptomeningeal contrast enhancement(LMCE)in the brain is observed in some pediatric patients during sedation for imaging.However,based on clinical history and cerebrospinal fluid analysis,the patients are not acutely ill and do not exhibit meningeal signs.Our study determined whether sevoflurane inhalation in pediatric patients led to this pattern of‘pseudo’LMCE(pLMCE)on 3 Tesla magnetic resonance imaging(MRI).AIM To highlight the significance of pLMCE in pediatric patients undergoing enhanced brain MRI under sedation to avoid misinterpretation in reports.METHODS A retrospective cross-sectional evaluation of pediatric patients between 0-8 years of age was conducted.The patients underwent enhanced brain MRI under inhaled sevoflurane.The LMCE grade was determined by two radiologists,and interobserver variability of the grade was calculated using Cohen’s kappa.The LMCE grade was correlated with duration of sedation,age and weight using the Spearman rho rank correlation.RESULTS A total of 63 patients were included.Fourteen(22.2%)cases showed mild LMCE,48(76.1%)cases showed moderate LMCE,and 1 case(1.6%)showed severe LMCE.We found substantial agreement between the two radiologists in detection of pLMCE on post-contrast T1 imaging(kappa value=0.61;P<0.001).Additionally,we found statistically significant inverse and moderate correlations between patient weight and age.There was no correlation between duration of sedation and pLMCE.CONCLUSION pLMCE is relatively common on post-contrast spin echo T1-weighted MRI of pediatric patients sedated by sevoflurane due to their fragile and immature vasculature.It should not be misinterpreted for meningeal pathology.Knowing pertinent clinical history of the child is an essential prerequisite to avoid radiological overcalling and the subsequent burden of additional investigations.

Research and Implementation of Algorithm for Image Enhancement and Unwrapped Distortion Correction for SLVF Panoramic Night Vision Image

Abstract： Based on digital signal processor（DSP） and field programmable gate array（FPGA） techniques, the architecture of super large view field（SLVF） panoramic night vision image processing hardware platform was established. The panoramic unwrapping and correcting algorithm, up to a full 360°, based on coordinate rotation digital computer （CORDIC） and night vision image enhancement algorithm, based on histogram equalization theory and edge detection theory, was presented in this paper, with the purpose of processing night vision dynamic panoramic annular image. The annular image can be unwrapped and corrected to conventional rectangular panorama by the panoramic image processing algorithm, which uses the pipelined CORDIC configuration to realize a trigonometric function generator with high speed and high precision. Histogram equalization algorithm can perfectly enhance the contrast of the night vision image. Edge detection algorithm can be propitious to find and detect small dim dynamic targets in night vision circumstances. After abundant experiment, the al- gorithm for panoramic image processing and night vision image enhancement is successfully implemented in FPGA and DSP. The panoramic night vision image system is a compact device, with no external rotating parts. And the system can reliably and dynamically detect 360＊ SLVF panoramic night vision image.

Underwater image enhancement based on red channel weighted compensation and gamma correction model

Due to the special characteristics of light in water,the information of the red channel is seriously attenuated in collected image.This causes other colors to dominate the image.This paper proposes an underwater image enhancement algorithm based on red channel weighted compensation and gamma correction model.Firstly,by analyzing the attenuation characteristics of RGB channels,the intensity and the edge information of red channel are compensated by weighting the attenuation coefficient ratio between different channels to correct the chromaticity.Then the gamma correction model is employed to stretch the intensity range to enhance the contrast which makes the image look clearer.The experimental results show that the proposed algorithm can correct the color cast effect and improve the contrast by nearly 2 times for the underwater images with too much red component attenuation.