IEEE Trans Image Process - Joint demosaicing and denoising via learned nonparametric random fields.

Tópicos

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Resumo

We introduce a machine learning approach to demosaicing, the reconstruction of color images from incomplete color filter array samples. There are two challenges to overcome by a demosaicing method: 1) it needs to model and respect the statistics of natural images in order to reconstruct natural looking images and 2) it should be able to perform well in the presence of noise. To facilitate an objective assessment of current methods, we introduce a public ground truth data set of natural images suitable for research in image demosaicing and denoising. We then use this large data set to develop a machine learning approach to demosaicing. Our proposed method addresses both demosaicing challenges by learning a statistical model of images and noise from hundreds of natural images. The resulting model performs simultaneous demosaicing and denoising. We show that the machine learning approach has a number of benefits: 1) the model is trained to directly optimize a user-specified performance measure such as peak signal-to-noise ratio (PSNR) or structural similarity; 2) we can handle novel color filter array layouts by retraining the model on such layouts; and 3) it outperforms the previous state-of-the-art, in some setups by 0.7-dB PSNR, faithfully reconstructing edges, textures, and smooth areas. Our results demonstrate that in demosaicing and related imaging applications, discriminatively trained machine learning models have the potential for peak performance at comparatively low engineering effort.

Resumo Limpo

introduc machin learn approach demosa reconstruct color imag incomplet color filter array sampl two challeng overcom demosa method need model respect statist natur imag order reconstruct natur look imag abl perform well presenc nois facilit object assess current method introduc public ground truth data set natur imag suitabl research imag demosa denois use larg data set develop machin learn approach demosa propos method address demosa challeng learn statist model imag nois hundr natur imag result model perform simultan demosa denois show machin learn approach number benefit model train direct optim userspecifi perform measur peak signaltonois ratio psnr structur similar can handl novel color filter array layout retrain model layout outperform previous stateoftheart setup db psnr faith reconstruct edg textur smooth area result demonstr demosa relat imag applic discrimin train machin learn model potenti peak perform compar low engin effort

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