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.

Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

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.

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.

A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

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.

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.

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.

Enhancement of Biomass Material Characterization Images Using an Improved U-Net

For scanning electronmicroscopes with high resolution and a strong electric field,biomass materials under observation are prone to radiation damage from the electron beam.This results in blurred or non-viable images,which affect further observation of material microscopic morphology and characterization.Restoring blurred images to their original sharpness is still a challenging problem in image processing.Traditionalmethods can’t effectively separate image context dependency and texture information,affect the effect of image enhancement and deblurring,and are prone to gradient disappearance during model training,resulting in great difficulty in model training.In this paper,we propose the use of an improvedU-Net(U-shapedConvolutional Neural Network)to achieve image enhancement for biomass material characterization and restore blurred images to their original sharpness.The main work is as follows:use of depthwise separable convolution instead of standard convolution in U-Net to reduce model computation effort and parameters;embedding wavelet transform into the U-Net structure to separate image context and texture information,thereby improving image reconstruction quality;using dense multi-receptive field channel modules to extract image detail information,thereby better transmitting the image features and network gradients,and reduce the difficulty of training.The experiments show that the improved U-Net model proposed in this paper is suitable and effective for enhanced deblurring of biomass material characterization images.The PSNR(Peak Signal-to-noise Ratio)and SSIM(Structural Similarity)are enhanced as well.

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be classified.Convolutional Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy.Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results.In this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and pneumonia.In this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia infection.In this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the dataset.The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the datasets.It achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.

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.

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.

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.

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.

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.

A Study on Enhancing Chip Detection Efficiency Using the Lightweight Van-YOLOv8 Network

In pursuit of cost-effective manufacturing,enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips.To ensure consistent chip orientation during packaging,a circular marker on the front side is employed for pin alignment following successful functional testing.However,recycled chips often exhibit substantial surface wear,and the identification of the relatively small marker proves challenging.Moreover,the complexity of generic target detection algorithms hampers seamless deployment.Addressing these issues,this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips,termed Van-YOLOv8.Initially,to alleviate the influence of diminutive,low-resolution markings on the precision of deep learning models,we utilize an upscaling approach for enhanced resolution.This technique relies on the Super-Resolution Generative Adversarial Network with Extended Training(SRGANext)network,facilitating the reconstruction of high-fidelity images that align with input specifications.Subsequently,we replace the original YOLOv8smodel’s backbone feature extraction network with the lightweight VanillaNetwork(VanillaNet),simplifying the branch structure to reduce network parameters.Finally,a Hybrid Attention Mechanism(HAM)is implemented to capture essential details from input images,improving feature representation while concurrently expediting model inference speed.Experimental results demonstrate that the Van-YOLOv8 network outperforms the original YOLOv8s on a recycled chip dataset in various aspects.Significantly,it demonstrates superiority in parameter count,computational intricacy,precision in identifying targets,and speed when compared to certain prevalent algorithms in the current landscape.The proposed approach proves promising for real-time detection of recycled chips in practical factory settings.

Scene 3-D Reconstruction System in Scattering Medium

Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions.The NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scattering media,is also evolving.Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency.This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction.First,we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across frames.Then,we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction results.After pose estimation using COLMAP,the selected keyframes undergo 3D reconstruction using neural radiance fields(NeRF)based on multi-resolution hash encoding for model construction and rendering.In terms of image enhancement,our method has been optimized in certain scenarios,demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same data.In terms of 3D reconstruction,our method achieved a peak signal-to-noise ratio(PSNR)of 18.40 dB and a structural similarity(SSIM)of 0.6677,indicating a good balance between operational efficiency and reconstruction quality.

Detecting early changes in choroidal vascularity and thickness using optical coherence tomography in patients with corneal crosslinking for keratoconus

AIM:To investigate changes in choroidal thickness and vascularity in keratoconus patients treated with corneal crosslinking.METHODS:This study evaluated 28 eyes of 22 patients with keratoconus who underwent corneal crosslinking.The choroidal thicknesses were evaluated on enhanced depth imaging optical coherence tomography at the preoperative and postoperative 3d,1,and 3mo.Choroidal thickness in the four cardinal quadrants and the fovea were evaluated.The choroidal vascularity index was also calculated.RESULTS:There was no significant difference in central choroidal thickness between the preoperative and postoperative 3d,1mo(P>0.05).There was a significant increase in the 3mo(P=0.034)and a significant decrease in the horizontal choroidal vascularity index on the postoperative 3d(P=0.014),there was no statistically significant change in vertical axes and other visits in horizontal sections(P>0.05).CONCLUSION:This study sheds light on choroidal changes in postoperative corneal crosslinking for keratoconus.While it suggests the procedure’s relative safety for submacular choroid,more extensive research is necessary to confirm these findings and their clinical significance.