@conference {Jancsary2012a,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
