Subjective Visual Quality Research Articles

Влияние различных типов плавающих помутнений стекловидного тела на субъективное качество зрения

Purpose. To compare subjective visual quality on patients with different types of vitreous floaters. Material and methods. The study included 68 patients (68 eyes). Three groups were formed: I – Weiss Ring – 24 eyes, II – Сloud opacities – 22 eyes, III – Solid conglomerates – 22 eyes. To evaluate the visual quality all subjects completed non-standartised questionnaire. Laser treatment was performed with the “Ultra Q Reflex” using pulse energy 3–7 mJ. Follow-up: at the baseline and, 1 months after treatment. Results. There was a statistically significant difference observed in I group in questions of floaters intensity and occurrence frequency compared to the II and III groups. In the II group there was a statistically significant difference in questions of spots size and number compare to the other floaters. In the III group there was a statistically significant difference of overall quality of life questions compare to the other patients. Comparative analysis of survey outcomes in groups I and II revealed a significant increase of subjective perceptions (p < 0,05) and a positive trend in groups III at 1 month follow-up after laser surgery. Conclusions. Patients with Weiss rings and cloud-like floaters have great psychological discomfort, with the little impact on the visual functions of patients. On the contrary, solid conglomerates, due to their structure, have a significant impact on the subjective quality of vision and impede daily activities. The initial type of floaters determines the tactics of management, as well as the results of laser treatment. Keywords: YAG laser vitreolysis, vitreous floaters, questionnaire.

Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution

Recently, deep-learning-based image super-resolution methods have made remarkable progress. However, most of these methods do not fully exploit the structural feature of the input image, as well as the intermediate features from the intermediate layers, which hinders the ability of detail recovery. To deal with this issue, we propose a gradient-guided and multi-scale feature network for image super-resolution (GFSR). Specifically, a dual-branch structure network is proposed, including the trunk branch and the gradient one, where the latter is used to extract the gradient feature map as structural prior to guide the image reconstruction process. Then, to absorb features from different layers, two effective multi-scale feature extraction modules, namely residual of residual inception block (RRIB) and residual of residual receptive field block (RRRFB), are proposed and embedded in different network layers. In our RRIB and RRRFB structures, an adaptive weighted residual feature fusion block (RFFB) is investigated to fuse the intermediate features to generate more beneficial representations, and an adaptive channel attention block (ACAB) is introduced to effectively explore the dependencies between channel features to further boost the feature representation capacity. Experimental results on several benchmark datasets demonstrate that our method achieves superior performance against state-of-the-art methods in terms of both subjective visual quality and objective quantitative metrics.

Objective Object Segmentation Visual Quality Evaluation: Quality Measure and Pooling Method

Objective object segmentation visual quality evaluation is an emergent member of the visual quality assessment family. It aims to develop an objective measure instead of a subjective survey to evaluate the object segmentation quality in agreement with human visual perception. It is an important benchmark for assessing and comparing the performances of object segmentation methods in terms of visual quality. Despite its essential role, sufficient study compared with other visual quality evaluation studies is still lacking. In this article, we propose a novel full-reference objective measure that includes a two-level single object segmentation visual quality measure and a pooling method for multiple object segmentation overall visual quality. The single object segmentation visual quality measure combines a pixel-level sub-measure and a region-level sub-measure for evaluating the similarity of area, shape, and object completeness between the segmentation result and the ground truth in terms of human visual perception. For the proposed multiple object segmentation overall visual quality pooling method, the rank of each object’s segmentation quality as a novel factor is integrated into the weighted harmonic mean to evaluate the overall quality. To evaluate the performance of our proposed measure, we tested it on an object segmentation subjective visual quality assessment database. The experimental results demonstrate that our proposed two-level measure and pooling method with good robustness perform better in matching subjective assessments compared with other state-of-the-art objective measures.

Subjective visual quality, reading fluency and patient satisfaction with the extended depth of focus IOL

Subjective visual quality, reading fluency and patient satisfaction with the extended depth of focus IOL

Extended Depth of Focus Versus Monofocal IOLs in Patients With High Myopia: Objective and Subjective Visual Outcomes.

To compare the objective and subjective outcomes between the extended depth of focus (EDOF) Mini Well intraocular lens (IOL) and the aspheric monofocal Mini-4-Ready IOL (both SIFI S.p.A.) in patients with high myopia. In this prospective comparative study, 40 patients with high myopia (axial length ≥ 26 mm) were enrolled: 20 patients were bilaterally implanted with the EDOF Mini Well IOL (EDOF group) and 20 patients were bilaterally implanted with the Mini-4-Ready IOL (monofocal group). Three-month follow-up data included corrected and uncorrected distance visual acuity at 4 m and 80, 67, and 40 cm, defocus curves, subjective and objective contrast sensitivity, objective optical quality (calculated with Optical Quality Analysis System; Visiometrics SL), halometry, and reading performance. Subjective visual quality was evaluated with National Eye Institute Refractive Error Quality of Life Instrument 42 scores. All visual acuities were significantly better in the EDOF group (P ⩽ .04) except monocular and binocular uncorrected and corrected distance visual acuities for distance (P ≥ .50). Defocus curves for myopic and hyperopic values were better in the EDOF group (P ⩽ .05), apart from +0.50 to -0.50 D (P ≥ .16). Contrast sensitivity curves was worse in the EDOF group in the mesopic-with-glare condition (P ⩽ .04). No differences were found in halometric values (P ≥ .15) and OQAS outcomes (P ≥ .47). National Eye Institute Refractive Error Quality of Life Instrument 42 subscale scores were better for expectation, near vision, activity limitations, and dependence on correction in the EDOF group (P ⩽ .04). Intermediate and near visual acuities were better in the EDOF group than in the monofocal group, with a comparable visual quality index between groups. [J Refract Surg. 2022;38(3):158-166.].

Visual Performance of a Novel Optical Design of a New Multifocal Intraocular Lens.

To evaluate the optical performance and quality of vision of a novel optical design of a new trifocal intraocular lens (IOL) using a proprietary modified algorithm. In this prospective, non-randomized, single-armed, single-center, open-label study, a total of 20 patients (40 eyes) who were candidates for multifocal lens implantation were recruited for implantation with the multifocal Intensity SL IOL (Hanita Lenses). Evaluation performed at 1 and 7 to 10 days and 1 and 3 months after implantation included corrected and uncorrected distance visual acuities at far, intermediate (80 cm), and near (40 cm). Monocular and binocular visual acuities, defocus curves, and contrast sensitivity were measured and questionnaires for grading subjective visual quality, satisfaction, and visual function were provided. Three months postoperatively, monocular uncorrected visual acuity for distance, intermediate, and near averaged 0.03 ± 0.11, 0.09 ± 0.09, and -0.22 ± 0.09 logMAR, respectively (Snellen 20/21.4, 20/24.6, and 20/12). Corrected monocular visual acuity for distance, intermediate, and near averaged -0.07 ± 0.06, 0.03 ± 0.09, and -0.25 ± 0.07 logMAR (20/17, 20/21.4, and 20/11.2), respectively. Binocular corrected visual acuity for distance, intermediate, and near was -0.1 ± 0.06, -0.02 ± 0.09, and -0.28 ± 0.04 logMAR (20/15.8, 20/19, and 20/10.5), respectively. Contrast sensitivity was similar to the normal population in photopic and mesopic conditions. Defocus curves showed that this IOL provided visual acuity of 20/28.9 or better between a defocus of +1.00 and -3.50 diopters. The Visual Function Index-14 questionnaire showed that patients reported high satisfaction. Patients specifically noted good quality of vision at near and intermediate distances. The Intensity SL IOL can provide good quality distance, intermediate, and particularly strong near vision after cataract surgery and independence from spectacles with good patient satisfaction. [J Refract Surg. 2022;38(3):150-157.].

Multiscale deep network for compressive sensing image reconstruction

Deep learning-based image compressive sensing methods have received extensive attention in recent years due to their superior learning ability and fast processing speed. The majority of existing image compressive sensing neural networks use single-scale sampling, whereas multiscale sampling has demonstrated excellent performance compared to single-scale. We propose a multiscale deep network for compressive sensing image reconstruction that consists of a multiscale sampling network and a reconstruction network. First, we use convolution to mimic the linear decomposition of images, and the convolution is learned during the training process. Then a sampling network captures compressive measurements across multiple decomposed scales. The reconstruction network, which includes both the initial and enhanced reconstruction networks, learns an end-to-end mapping between the compressed sensing (CS) measurements and the recovered images of the network. Experimental results indicate that the proposed network framework outperforms the existing CS methods in terms of objective metrics, peak signal to noise ratio (PSNR), structural similarity index, and subjective visual quality. Specifically, at a 0.1 sampling rate, using 10 images for testing, and the average PSNR maximum (minimum) gain is 5.95 dB (0.25 dB).

Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images.

Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.

Denoising 3D magnetic resonance images based on weighted tensor nuclear norm minimization using balanced nonlocal patch tensors

The denoising of magnetic resonance (MR) images is important to improve the accuracy of organ tissue information recognition in the process of medical diagnosis. This paper proposes a 3D MR image denoising method based on weighted tensor nuclear norm minimization using balanced nonlocal patch tensors. In order to make utilization of the nonlocal self-similarity in high-dimensional images and the correlation among different dimensions, a high-dimensional adaptive clustering technology is developed to construct highly correlated 3D MR image patches into nonlocal patch tensors. Since the generated tensor exhibits low-rank characteristics, a low-rank tensor approximation technique based on the tensor singular value decomposition framework can be used to denoise the MR data. To improve the denoising accuracy, the local noise level information of different nonlocal patch tensors is considered. Besides, a method for constructing more balanced 3D nonlocal patch tensors is proposed to exploit the effectiveness of the tensor singular value decomposition. Experimental results show that the proposed method has a greater improvement over the existing MR image denoising methods in terms of objective measurement and subjective visual quality on simulated and real 3D MR images with different categories and noise levels.

Medical image fusion based on improved multi-scale morphology gradient-weighted local energy and visual saliency map

Medical image fusion refers to the process of fusing images of different modes of the same object using image processing technology to maximize the mining of image information and improve image quality. At present, many methods are available to study image fusion, but they usually have shortcomings such as low image contrast and a weak ability to retain image details and edges. To solve these problems, we propose a new multimodal medical image fusion method. In this algorithm, the original image is decomposed into high-frequency and low-frequency information by non-subsampled shearlet transform (NSST). Low-frequency information is fused by visual saliency maps, which avoids edge loss caused by direct use of the coefficient maximum fusion rule. High-frequency information is fused by a method jointly guided by the improved multi-scale morphology gradient and weighted sum of eight-neighborhood-based modified Laplacian, which retains the texture details and the edge of the image. Finally, the fused image is generated by the NSST inverse transform. This strategy solves the problem of insufficient detail extraction in traditional algorithms, improves the overall appearance of the fused image, and enhances the contrast. To verify the effectiveness of the algorithm, we applied this technique to four different medical image modality combinations, compared the results with nine image fusion methods published in recent years, and evaluated the fused images using image quality evaluation indexes. Our algorithm achieved better results in terms of subjective vision and objective image quality evaluations and therefore should be competitive with existing technologies.

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images.

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

Deep Hyperspectral Image Fusion Network With Iterative Spatio-Spectral Regularization

Physical acquisition of high-resolution hyperspectral images (HR-HSI) has remained difficult, despite its potential of resolving material-related ambiguities in vision applications. Deep hyperspectral image fusion, aiming at reconstructing an HR-HSI from a pair of low-resolution hyperspectral image (LR-HSI) and high-resolution multispectral image (HR-MSI), has become an appealing computational alternative. Existing fusion methods either rely on hand-crafted image priors or treat fusion as a nonlinear mapping problem, ignoring important physical imaging models. In this paper, we propose a novel regularization strategy to fully exploit the spatio-spectral dependency by a spatially adaptive 3D filter. Moreover, the joint exploitation of spatio-spectral regularization and physical imaging models inspires us to formulate deep hyperspectral image fusion as a differentiable optimization problem. We show how to solve this optimization problem by an end-to-end training of a model-guided unfolding network named DHIF-Net. Unlike existing works of simply concatenating spatial with spectral regularization, our approach aims at an end-to-end optimization of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iterative</i> spatio-spectral regularization by multistage network implementations. Our extensive experimental results on both synthetic and real datasets have shown that our DHIF-Net outperforms other competing methods in terms of both objective and subjective visual quality.

FPD Net: Feature Pyramid DehazeNet

We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and subjective visual quality. In addition, it achieved a good performance in speed by using EfficientNet B0 as a feature extractor. We find that only using high-level semantic features can not effectively obtain all the information in the image. The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics, and can better take into account the global and local features. The five feature maps with different sizes are not simply weighted and fused. In order to keep all their information, we put them all together and get the final features through decode layers. At the same time, we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN. It is proved that EfficientNet with BiFPN can obtain image features more efficiently. Therefore, EfficientNet with BiFPN is chosen as our network feature extraction.

Learning Enriched Features for Image Super Resolution

In recent years, significant progress has been made in image super-resolution (SR) methods based on convolutional neural networks. However, most of them do not fully utilize multi-scale feature correspondence in the image SR process, resulting in blurred and artifact detail restoration, especially for SR tasks with larger scaling factors (i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 8$ </tex-math></inline-formula> ). A multi-scale feature-enhanced SR network (MFENet) is proposed to solve the problems mentioned above. Specifically, the super-resolution method is enhanced by utilizing multi-scale feature correspondence in two ways. On the one hand, we propose an effective multi-scale non-local attention module and it can fully exploit a large number of low-level features at different scales of a single low-resolution (LR) feature map by exhaustively evaluating the correlations in local feature, in-scale non-local feature, and cross-scale non-local feature. On the other hand, the mapping from high-resolution (HR) to LR images is explored by a feedback branch we introduced, that provides an additional constraint for images reconstruction. It can penalize incorrect SR predictions to achieve better SR performance. Experimental results on 5 benchmark datasets show that our method outperforms state-of-the-art methods in terms of both subjective visual quality and objective quantitative metrics.

Deep Light Field Super-Resolution Using Frequency Domain Analysis and Semantic Prior

Light field (LF) camera can simultaneously capture the intensity and direction information of light rays, which has been widely concerned. However, limited by the size of the imaging sensor, the captured LF image (LFI) has a trade-off between spatial and angular resolutions. To this end, this paper proposes a new LF super-resolution method using frequency domain analysis and semantic prior, which designs a two-stage learning framework to enhance the spatial and angular resolutions of LFI. Specifically, the proposed method first decomposes the spatial and angular information to explore the 4D structure of LFI by using frequency domain transformation, and formulates the LF super-resolution as a frequency restoration process. Then, the decomposed frequency components are recovered in a progressive restoration manner, with new cascaded 2D and 3D convolutional neural networks. To further improve the quality of the reconstructed LFI, especially at the object boundary, the semantic prior is incorporated into the designed network to enhance its representation ability. Finally, the super-resolved LFI is reconstructed by inverse frequency domain transformation. Experimental results show that the proposed method can effectively generate high-resolution LFI, and outperforms other state-of-the-art methods in terms of both subjective visual perception and objective quality evaluation. Moreover, the proposed method can enhance the performance of LF applications such as depth estimation.

Tensor Product and Tensor-Singular Value Decomposition Based Multi-Exposure Fusion of Images

Considering multidimensional structure of the multi-exposure images, a new Tensor product and Tensor-singular value decomposition based Multi-Exposure image Fusion (TT-MEF) method is proposed. The main innovation of this work is to explore a new feature representation of multi-exposure images in the new tensor domain and design the fusion strategy on this basis. Specifically, the luminance and the chrominance channels are fused separately to maintain color consistency. For the luminance fusion, the luminance channel of multi-exposure images is divided into two parts, that is, de-mean term and mean term. The de-mean term is represented as a tensor to extract the feature. Then, the tensor product and tensor-singular value decomposition (T-SVD) are used to design a tensor feature extractor. Furthermore, a fusion strategy of the de-mean term is presented according to the visual saliency model, and a fusion strategy of the mean term is defined by the local and the global visual weights to control counterpoise between the local and global luminance. For the chrominance fusion, a new fusion strategy is also designed by the tensor product and T-SVD, similar to the luminance fusion. Finally, the fused image is obtained by combining the luminance and chrominance fusion. Experimental results show that the proposed TT-MEF method generally outperforms the existing state-of-the-art in terms of subjective visual quality and objective evaluation.

Comparative analysis of visual outcome with 3 intraocular lenses: monofocal, enhanced monofocal, and extended depth of focus.

To compare the visual performance, spectacle independence, and subjective visual quality of 3 intraocular lenses (IOLs): monofocal, enhanced monofocal, and extended depth of focus. Ophthalmology Department, San Raffaele Scientific Institute, Milan, Italy. Prospective case series. Patients without ocular comorbidities and corneal astigmatism <0.75 diopters (D) undergoing cataract surgery with bilateral implantation of monofocal Tecnis ZCB00, enhanced monofocal Eyhance ICB00, and extended depth-of-focus Symfony ZXR00 IOLs were included. 6 months postoperatively, the following parameters were analyzed: subjective and objective refraction; monocular and binocular corrected distance (4 m) and uncorrected distance (UDVA) visual acuity; corrected distance, intermediate (66 cm), and near (40 cm) visual acuities, as well as uncorrected intermediate (UIVA) and uncorrected near (UNVA) visual acuities; photopic contrast sensitivity; binocular defocus curve; halo and glare perception; and spectacle independence. 150 eyes of 75 patients (25 patients per IOL group) were evaluated. Monocular and binocular UDVA was excellent in all 3 groups. Eyhance achieved binocular UIVA similar to that of Symfony, the latter showing the highest binocular UNVA. The defocus curves at -1.0 D were equivalent for both Eyhance and Symfony, whereas contrast sensitivity was similar in all 3 groups. The Eyhance spectacle independence score was comparable with Symfony for intermediate distance activities, the latter, however, achieved the worst results for halos and glare. Enhanced monofocal Eyhance was not inferior to Symfony as regards intermediate distance visual outcome and spectacle independence, and also provided lower subjective perception of halos and glare.

Rain-component-aware capsule-GAN for single image de-raining

Images taken in the rain usually have poor visual quality, which may cause difficulties for vision-based analysis systems. The research aims to recover clean image content from a single rainy image by removing rain components without introducing any artifacts. Existing rain removal methods often model the rain component as noise, but it obviously has clear patterns instead of random noise. Motivated by this, we raise the idea to build modules to capture rain patterns for de-raining. A Rain-Component-Aware (RCA) network is proposed to capture the characteristics of the rain. We then integrate it into an image-conditioned generative adversarial network (image-cGAN) as a RCA loss to guide the generation of rainless images. This results in the proposed two-branch cGAN, where one branch aims at improving the image visual quality after de-raining, and the other aims at extracting rain patterns so that the rain could be effectively removed. To better capture the spatial relationship of different objects within an image, we incorporate the capsule structure in both generator and discriminator of cGAN, which further improves the quality of generated images. The proposed approach is hence named as RCA-cGAN. Benefited by the RCA loss based two-branch optimization and the capsule structure, RCA-cGAN achieves good de-raining effect. Extensive experimental results on several benchmark datasets show that the RCA network is effective to capture rain patterns and the proposed approach could produce much better de-raining images in terms of both subjective visual quality inspection and objective quantitative assessment.

Digital Variance Angiography in Selective Lower Limb Interventions

PurposeTo evaluate the potential benefits of digital variance angiography (DVA) in selective lower limb angiography and to compare the performance of 2 DVA algorithms (conventional DVA1 and the recently developed DVA2) to that of digital subtraction angiography (DSA). Materials and MethodsFrom November 2019 to May 2020, 112 iodinated contrast media (ICM) and 40 carbon dioxide (CO2) angiograms were collected from 15 and 13 peripheral artery disease patients, respectively. The DVA files were retrospectively generated from the same unsubtracted source file as DSA. The objectively calculated contrast-to-noise ratio (CNR) and the subjective visual image quality of DSA, DVA1, and DVA2 images were statistically compared using the Wilcoxon signed-rank test. The images were evaluated by 6 radiologists (R.P.T., S.V., A.M.K., S.S.A., O.E., and J.S.) from 2 centers using a 5-grade Likert scale. ResultsBoth DVA algorithms produced similar increase (at least 2-fold) in CNR values (P < .001) and significantly higher image quality scores than DSA, independent of the contrast agent used. The overall scores with ICM were 3.61 ± 0.05 for DSA, 4.30 ± 0.04 for DVA1, and 4.33 ± 0.04 for DVA2 (each P < .001 vs DSA). The scores for CO2 were 3.10 ± 0.14 for DSA, 3.63 ± 0.13 for DVA1 (P < .001 vs DSA), and 3.38 ± 0.13 for DVA2 (P < .05 vs DSA). ConclusionsDVA provides higher CNR and significantly better image quality in selective lower limb interventions irrespective of the contrast agent used. Between DVA algorithms, DVA1 is preferred because of its identical or better image quality than DVA2. DVA can potentially help the interventional decision process and its quality reserve might allow dose management (radiation/ICM reduction) in the future.

Pure ICL Implantation: A Novel Ophthalmic Viscosurgical Device-Free Method.

Purpose To assess the clinical efficiency of a novel ophthalmic viscosurgical device-free (OVD-free) method for intraocular collamer lens (EVO-ICL) implantation in myopic eyes. Methods In this study, 40 patients underwent ICL implantation for both eyes: one eye underwent traditional ICL implantation, and the other eye underwent OVD-free (pure) ICL implantation. Preoperative and postoperative UDVA, BCVA, equivalent spherical degree (SE), IOP, visual quality index, subjective visual quality scale, corneal endothelial cell density (ECD), operation time, and complications were compared between and within the traditional and pure ICL implantation groups. Results Increased IOP >22 mmHg 2 h after surgery was noted in 8 eyes (20%) in the traditional group, but not in the pure group (0%, P < 0.001). Increased IOP relative to baseline was significantly higher at 2 h after surgery for the traditional group compared with the pure group (P < 0.001). UDVA, BCVA, and SE were significantly improved in the pure group compared with those in the traditional group 1 day (P < 0.001, P=0.003) after implantation, but not 1 week or 3 months after. Modulation transfer function cut-off frequency (MTF cut-off), Strehl ratio (SR), and OV20% were significantly better in the pure group than in the traditional group 1 day after implantation (P=0.013, P=0.009, and P=0.004). No significant difference in ECD changes within or between groups was observed (P > 0.05). The operation time for the pure group (2.897 ± 0.346 min) was significantly shorter than that for the traditional group (4.444 ± 0.656 min; P < 0.001). No complications were reported for either group during the observation period, except early IOP elevation in the traditional group. Conclusions The pure ICL implantation method was associated with faster visual acuity recovery, shorter operation time, and more stable intraocular pressure. Pure ICL represents a safe and convenient method for ICL implantation compared with the traditional method, completely eliminating OVD-related complications without causing additional complications.