Cloud Removal from Satellite Images Using a Deep Learning Model with the Cloud-Matting Method

Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image’s information

Cloud Removal from Satellite Images Using a Deep Learning Model with the Cloud-Matting Method

Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image’s information

Catalytic conversion of cellulose to C-5/C-6 alkanes over Ir-VOx/SO2 combined with HZSM-5 in n-dodecane/water system

The liquid fuel (made of C-5/C-6 alkanes) was obtained directly from the hydrogenolysis of microcrystalline cellulose (MCC) with Ir-VOx/SiO2 combined with HZSM-5 as the composite catalyst in a biphasic system (n-dodecane + H2O). The performance of the catalyst was investigated by carrying out a series of experiments using various V/Ir molar ratios, catalyst dosages, reaction temperatures, reaction time, hydrogen pressure and substrates. At the optimized conditions, the cellulose was almost completely converted, and at the same time, a high C-5/C-6 yield of 85.1% was obtained at 210 degrees C for 24 h and 6 MPa with the V/Ir molar ratio being 0.13. These results not only proved that Ir-VOx/SiO2 (V/Ir = 0.13) has excellent performance for the hydrogenolysis of MCC to liquid alkanes, but also indicated that vanadium is a good metal promoter for iridium. In addition, it was proven the C-5/C-6 alkanes were obtained via sorbitol through the combined effect of Ir-VOx/SiO2 and HZSM-5

Efficacy of topical 0.05% cyclosporine A and 0.1% sodium hyaluronate in post-refractive surgery chronic dry eye patients with ocular pain

Abstract Background The management of post-refractive surgery dry eye disease (DED) can be challenging in clinical practice, and patients usually show an incomplete response to traditional artificial tears, especially when it is complicated with ocular pain. Therefore, we aim to investigate the efficacy of combined topical 0.05% cyclosporine A and 0.1% sodium hyaluronate treatment in post-refractive surgery DED patients with ocular pain unresponsive to traditional artificial tears. Methods We enrolled 30 patients with post-refractive surgery DED with ocular pain who were unresponsive to traditional artificial tears. Topical 0.05% cyclosporine A and 0.1% sodium hyaluronate were used for 3 months. They were evaluated at baseline and 1 and 3 months for dry eye and ocular pain symptoms and objective parameters, including Numerical Rating Scale (NRS), Neuropathic Pain Symptom Inventory modified for the Eye (NPSI-Eye), tear break-up time (TBUT), Schirmer I test (SIt), corneal fluorescein staining (CFS), corneal sensitivity, and corneal nerve morphology. In addition, tear levels of inflammatory cytokines and neuropeptides were measured using the Luminex assay. Results After 3 months of treatment, patients showed a statistically significant improvement in the ocular surface disease index (OSDI), TBUT, SIt, CFS, and corneal sensitivity (all P < 0.01) using linear mixed models. As for ocular pain parameters, the NRS and NPSI-Eye scores were significantly reduced (both P < 0.05) and positively correlated with the OSDI and CFS scores. Additionally, tear IL-1β, IL-6, and TNF-α levels were improved better than pre-treatment (P = 0.01, 0.03, 0.02, respectively). Conclusion In patients with post-refractive surgery DED with ocular pain, combined topical 0.05% cyclosporine A and 0.1% sodium hyaluronate treatment improved tear film stability, dry eye discomfort, and ocular pain, effectively controlling ocular inflammation. Trial registration Registration number: NCT06043908

Comparison of binocular visual quality in six treatment protocols for bilateral cataract surgery with presbyopia correction: a prospective two-center single-blinded cohort study

AbstractObjective To compare the postoperative binocular visual quality in six treatment protocols for bilateral age-related cataract surgery with presbyopia correction for clinical decisions.Materials and methods In this prospective two-center single-blinded cohort study, participants from North or South China who underwent bilateral phacoemulsification and intraocular lens implantation were divided into six protocols: monovision, diffractive bifocal, mixed, refractive bifocal, trifocal, and micro-monovision extended range of vision (EROV). Binocular visual quality was evaluated at 3 months postoperatively, including binocular uncorrected full-range visual acuity, binocular defocus curves (depth of focus [DoF] and area under the curve [AUC]), binocular visual function (fusion function and stereopsis), binocular subjective spectacle independence rates, visual analog scale (VAS) of overall satisfaction, 25-item visual function questionnaire (VFQ-25), and binocular dysphotopsia symptoms.Results Of the 300 enrolled patients, 272 (90.7%; 544 eyes) were analyzed. The trifocal protocol showed excellent binocular full-range visual acuity and the best performance for most DoFs and AUCs. The monovision protocol presented the worst binocular visual quality in most perspectives, especially in convergence, distance, and near stereopsis (p  0.05). The EROV protocol achieved the highest VAS and VFQ-25 scores. The incidence of postoperative binocular dysphotopsia symptoms was comparable in all protocols.Conclusions The trifocal protocol showed the best performance, and the monovision protocol presented the worst performance in most perspectives of binocular visual quality for presbyopia correction. The refractive bifocal, mixed, or EROV protocols can provide an approximate performance as a trifocal protocol. Ophthalmologists can customize therapies using different protocols

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy:a development and validation study

Aims/hypothesis Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics. Methods Our deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy. Results The intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria). Conclusions/interpretation These results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy. Data availability The publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal% 20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm

Comparison of binocular visual quality in six treatment protocols for bilateral cataract surgery with presbyopia correction: a prospective two-center single-blinded cohort study

To compare the postoperative binocular visual quality in six treatment protocols for bilateral age-related cataract surgery with presbyopia correction for clinical decisions. In this prospective two-center single-blinded cohort study, participants from North or South China who underwent bilateral phacoemulsification and intraocular lens implantation were divided into six protocols: monovision, diffractive bifocal, mixed, refractive bifocal, trifocal, and micro-monovision extended range of vision (EROV). Binocular visual quality was evaluated at 3 months postoperatively, including binocular uncorrected full-range visual acuity, binocular defocus curves (depth of focus [DoF] and area under the curve [AUC]), binocular visual function (fusion function and stereopsis), binocular subjective spectacle independence rates, visual analog scale (VAS) of overall satisfaction, 25-item visual function questionnaire (VFQ-25), and binocular dysphotopsia symptoms. Of the 300 enrolled patients, 272 (90.7%; 544 eyes) were analyzed. The trifocal protocol showed excellent binocular full-range visual acuity and the best performance for most DoFs and AUCs. The monovision protocol presented the worst binocular visual quality in most perspectives, especially in convergence, distance, and near stereopsis (p p > 0.05). The EROV protocol achieved the highest VAS and VFQ-25 scores. The incidence of postoperative binocular dysphotopsia symptoms was comparable in all protocols. The trifocal protocol showed the best performance, and the monovision protocol presented the worst performance in most perspectives of binocular visual quality for presbyopia correction. The refractive bifocal, mixed, or EROV protocols can provide an approximate performance as a trifocal protocol. Ophthalmologists can customize therapies using different protocols.</p

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation stud