Improved performance of inverse halftoning algorithms via coupled dictionaries

Inverse halftoning techniques are known to introduce visible distortions (typically, blurring or noise) into the reconstructed image. To reduce the severity of these distortions, we propose a novel training approach for inverse halftoning algorithms. The proposed technique uses a coupled dictionary (CD) to match distorted and original images via a sparse representation. This technique enforces similarities of sparse representations between distorted and non-distorted images. Results show that the proposed technique can improve the performance of different inverse halftone approaches. Images reconstructed with the proposed approach have a higher quality, showing less blur, noise, and chromatic aberrations.