Effect of visual perception training on binocular visual function reconstruction in patients after strabismus surgery

AIM:To explore the effect of visual perception learning software training(VPT)on binocular visual function reconstruction in children with intermittent exotropia after strabismus surgery.METHODS:Ninety children with intermittent exotropia admitted to our hospital from June 2018 to December 2018 were included,and randomly divided into VPT and control groups.Children in the control group received basic binocular vision training,while those in the VPT group received VPT after strabismus surgery.Tertiary visual function,visual perception function,Newcastle Control Score(NCS),and ocular position retraction rate were compared at 3 and 12mo after the surgery.RESULTS:At 3 and 12mo after the surgery,the proportion of simultaneous perception,binocular fusion version and binocular stereo vision in the VPT group was conspicuously higher than that in the control group(P<0.05).After the vision training,the binocular visual perception functions of children in both groups were significantly improved compared with that before training(P<0.05).Interestingly,the grating sharpness,texture perception and texture motion perception in the VPT group were dramatically better than control group(P<0.01).The NCS in the VPT group was significantly lower than that in the control group(P<0.05).The ocular position retraction rate in the VPT group was significantly lower than that in the control group at 12mo(8.89%vs 26.67%,P=0.03).CONCLUSION:VPT effectively promotes binocular visual function reconstruction in intermittent exotropia children after strabismus surgery and reduces the strabismus severity and ocular position retraction rate.

No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics

A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.