This paper proposes a compiler generation method for PEAS-III (Practical Environment for ASIP development), which is a configurable processor development environment for application domain specific embedded systems. Using the PEAS-III system, not only the HDL description of a target processor but also its target compiler can be generated. Therefore, execution cycles and dynamic power consumption can be rapidly evaluated. Two processors and their derivatives were designed using the PEAS-III system in the experiment. Experimental results show that the trade-offs among area, performance and power consumption of processors were analyzed in about twelve hours and the optimal processor was selected under the design constraints by using generated compilers and processors.

We propose an iterative single-image haze-removal method that first divides images with haze into regions in which haze-removal processing is difficult and then estimates the ambient light. The existing method has a problem wherein it often overestimates the amount of haze in regions where there is a large distance between the location the photograph was taken and the subject of the photograph; this problem prevents the ambient light from being estimated accurately. In particular, it is often difficult to accurately estimate the ambient light of images containing white and sky regions. Processing those regions in the same way as other regions has detrimental results, such as darkness or unnecessary color change. The proposed method divides such regions in advance into multiple small regions, and then, the ambient light is estimated from the small regions in which haze removal is easy to process. We evaluated the proposed method through some simulations, and found that the method achieves better haze reduction accuracy even than the state-of-the art methods based on deep learning.