Handbook of image processing operators
Handbook of image processing operators
A cubic unsharp masking technique for contrast enhancement
Signal Processing
Contrast enhancement in images via the product of linear filters
Signal Processing
Medical image segmentation in digital mammography
Advanced algorithmic approaches to medical image segmentation
Digital Image Processing
Explosives detection systems (EDS) for aviation security
Signal Processing
Image Enhancement Using Color and Spatial Information
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
IEEE Transactions on Information Technology in Biomedicine
Image enhancement via adaptive unsharp masking
IEEE Transactions on Image Processing
Visual words on baggage X-ray images
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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Image enhancement is very important for increasing the sensitivity of screening luggage performance at airports. On the basis of 11 statistical measures of image viewability we propose a novel approach to optimizing the choice of image enhancement tools. We propose a neural network predictor that can be used for predicting, on a given test image, the best image enhancement algorithm for it. The network is trained using a number of image examples. The input to the neural network is a set of viewability measures and its output is the choice of enhancement algorithm for that image. On a number of test images we show that such a predictive system is highly capable in forecasting the correct choice of enhancement algorithms (as judged by human experts). We compare our predictive system against a baseline approach that uses a fixed enhancement algorithm for all batch test images, and find the proposed model to be substantially superior.