Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations. Qualitative experiments show that, contrary to ordinary autoencoders, denoising autoencoders are able to learn Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. It is however shown on a benchmark of classification problems to yield significantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it.
The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs.
For future work, the SDM could be employed to downscale the mean ensemble projections of different climate variables. In conclusion, the SDM showed better performance reproducing the projections of the mean ensemble rather than the individual RCMs. The SDM showed good performance in reproducing the temporal rainfall projections, whereas unsatisfactory simulation was recorded regarding the spatial rainfall projections. Similarly, the bias ratio scored a low value ranging from -8.94 < bias < 8.25. The SDM has exhibited positive correlation of 0.75 < R < 0.95 and low RMSE values ranging between 6.9 and 15.8 mm/month. Continuous statistics were employed to examine the SDM performance. To evaluate the SDM performance emulating latest Rossby Centre (RCA4) RCM, SDM results were investigated against RCM projection products (2006-2100). Historical rainfall simulation for the period 1951-2005 from 8 GCMs were applied to train/validate the SDM. Eight General Circulation Models (GCMs) rainfall datasets were selected under the Representative Concentration Pathway (RCP4.5) emission scenario over Northern Africa. This study employed Machine Learning (ML) technique known as Convolutional Autoencoder to build Statistical Downscaling Model (SDM) emulator.
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