ABSTRACT In response to the issues of poor sharpness and low information entropy in traditional MSRCP (Multi‐Scale Retinex with Color Restoration) algorithms for image enhancement, we propose an improved MSRCP algorithm for low‐light image enhancement with chromaticity preservation. First, we replaced the extrema calculation method in the color restoration function with a calculation method based on clipped pixel ratios. Then, we combined guided filtering and Gaussian filtering to calculate the incident component. Finally, we conducted experiments using six different low‐light images and compared the results with the traditional MSRCP algorithm, such as SSR, MSR, MSRCR, and MSRCP. The experimental results show that our method improved the sharpness and information entropy values in the five comparison images by 5.6%–35.6% and 0.18%–15.3%, respectively.

Traditionally, touchscreen typing has been studied in terms of motor performance. However, recent research has exposed a decisive role of visual attention being shared between the keyboard and the text area. Strategies for this are known to adapt to the task, design, and user. In this paper, we propose a unifying account of touchscreen typing, regarding it as optimal supervisory control. Under this theory, rules for controlling visuo-motor resources are learned via exploration in pursuit of maximal typing performance. The paper outlines the control problem and explains how visual and motor limitations affect it. We then present a model, implemented via reinforcement learning, that simulates co-ordination of eye and finger movements. Comparison with human data affirms that the model creates realistic finger- and eye-movement patterns and shows human-like adaptation. We demonstrate the model’s utility for interface development in evaluating touchscreen keyboard designs.

Relatively little is known about eye and finger movement in typing with mobile devices. Most prior studies of mobile typing rely on log data, while data on finger and eye movements in typing come from studies with physical keyboards. This paper presents new findings from a transcription task with mobile touchscreen devices. Movement strategies were found to emerge in response to sharing of visual attention: attention is needed for guiding finger movements and detecting typing errors. In contrast to typing on physical keyboards, visual attention is kept mostly on the virtual keyboard, and glances at the text display are associated with performance. When typing with two fingers, although users make more errors, they manage to detect and correct them more quickly. This explains part of the known superiority of two-thumb typing over one-finger typing. We release the extensive dataset on everyday typing on smartphones.