Comparison of Deep Learning and Conventional Demosaicing Algorithms for Mastcam Images

• Published in: Electronics (Basel) 2019-03, Vol.8 (3), p.308
• Main Authors: Kwan, Chiman, Chou, Bryan, Bell III, James
• Format: Article
• Language: English
• Subjects: Aeronautics; Algorithms; Business metrics; Color; Comparative studies; Curiosity (Mars rover); Deep learning; Digital cameras; Digital imaging; Image filters; Image quality; Machine learning; Mars rovers; Methods; Neural networks; Quality assessment
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics8030308

Bayer pattern filters have been used in many commercial digital cameras. In National Aeronautics and Space Administration’s (NASA) mast camera (Mastcam) imaging system, onboard the Mars Science Laboratory (MSL) rover Curiosity, a Bayer pattern filter is being used to capture the RGB (red, green, and blue) color of scenes on Mars. The Mastcam has two cameras: left and right. The right camera has three times better resolution than that of the left. It is well known that demosaicing introduces color and zipper artifacts. Here, we present a comparative study of demosaicing results using conventional and deep learning algorithms. Sixteen left and 15 right Mastcam images were used in our experiments. Due to a lack of ground truth images for Mastcam data from Mars, we compared the various algorithms using a blind image quality assessment model. It was observed that no one algorithm can work the best for all images. In particular, a deep learning-based algorithm worked the best for the right Mastcam images and a conventional algorithm achieved the best results for the left Mastcam images. Moreover, subjective evaluation of five demosaiced Mastcam images was also used to compare the various algorithms.