This paper presents a novel image restoration algorithm using examples and truncated constrained least
squares (TCLS) filter for ultra-high definition (UHD) television systems. The proposed approach consists
of three steps: (i) generation of the patch dictionary using multiple-step image blurring, (ii) selection of
the optimum patch based on the orientation and the amount of blurring, and (iii) combination of the
selected patch in the dictionary and its filtered version by the TCLS restoration filter for reducing the
patch mismatch error. In the proposed algorithm, a complicated point-spread-function (PSF) estimation
process is replaced with the generation of multiple, differently blurred patches. Furthermore, the patch
dictionary is made by orientation-based classification to reduce the time to search the optimum patch.
Experimental results show that the proposed algorithm can restore more natural images with less
synthetic artifacts than existing methods. The proposed method provides a significantly improved
restoration performance over existing methods in the sense of both subjective and objective measures
including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM)