Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Entropy Approach in the Analysis of Anisotropy of Digital Images
Open Systems & Information Dynamics
Evaluation of the effects of Gabor filter parameters on texture classification
Pattern Recognition
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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A new algorithm for image quality assessment based on entropy of Gabor filtered images is proposed. A bank of Gabor filters is used to extract contours and directional textures. Then, the entropy of the images obtained after the Gabor filtering is calculated. Finally, a metric for the image quality is proposed. It is important to note that the quality of the image is image content-dependent, so our metric must be applied to variations of the same scene, like in image acquisition and image processing tasks. This process makes up an interesting tool to evaluate the quality of image acquisition systems or to adjust them to obtain the best possible images for further processing tasks. An image database has been created to test the algorithm with series of images degraded by four methods that simulate image acquisition usual problems. The presented results show that the proposed method accurately measures image quality, even with slight degradations.