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Color fundus image quality greatly influence the doctors' diagnostic accuracy. However, the problems of imbalance data and small sample are the key issues of the color fundus images quality assessment. Hence, this paper purposes a small sample color fundus image quality assessment based on gcforest to solve these problems. Firstly, this paper extracts color and texture features to represent the quality of color fundus image. Next, re-sampling process is used to re-balance training data. Thirdly, the training data after re-balanced is sent to train gcforest which is a forest integration model. Finally, the trained gcforest which is good for small sample problem is used to evaluate color fundus images quality. Experiments demonstrate that the proposed method not only in color fundus image quality assessment but also in glaucoma classification task get good results.

Adaptive Optics together with subsequent post-processing techniques obviously improve the resolution of turbulencedegraded images in ground-based space objects detection and identification. The most common method for frame selection and stopping iteration in post-processing has always been subjective viewing of the images due to a lack of widely agreed-upon objective quality metric. Full reference metrics are not applicable for assessing the field data, noreference metrics tend to perform poor sensitivity for Adaptive Optics images. In the present work, based on the Laplacian of Gaussian (LOG) local contrast feature, a nonlinear normalization is applied to transform the input image into a normalized LOG domain; a quantitative index is then extracted in this domain to assess the perceptual image quality. Experiments show this no-reference quality index is highly consistent with the subjective evaluation of input images for different blur degree and different iteration number.