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With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications. [ABSTRACT FROM AUTHOR]

Rain removal is very important for many applications in computer vision, and it is a challenging problem due to its ill‐posed nature, especially for single‐image deraining. In order to remove rain streaks more thoroughly, as well as to retain more details, a progressive dilation dense residual fusion network is proposed. The entire network is designed in a cascade manner with multiple fusion blocks. The fusion block consists of a dilation dense residual block (DDRB) and a dense residual feature fusion block (DRFFB), where DDRB is created for feature extraction and DRFFB is mainly designed for feature fusion operation. Meanwhile, detail compensation memory mechanism (DCMM) is leveraged between each of two cascade modules to retain more background details. Compared with previous state‐of‐the‐art methods, extensive experiments show that the proposed method can achieve better results, in terms of rain streaks removal and background details preservation. Furthermore, the authors' network also shows its superiority for image noise removal. [ABSTRACT FROM AUTHOR]

A visual prosthesis is an auxiliary device for patients with blinding diseases that cannot be treated with conventional surgery or drugs. It converts captured images into corresponding electrical stimulation patterns, according to which phosphenes are generated through the action of internal electrodes on the visual pathway to form visual perception. However, due to some restrictions such as the few implantable electrodes that the biological tissue can accommodate, the induced perception is far from ideal. Therefore, an important issue in visual prosthesis research is how to detect and present useful information in low‐resolution prosthetic vision to improve the visual function of the wearer. In recent years, with the development and broad application of computer vision methods, researchers have investigated the possibility of their utilization in visual prostheses by simulating prosthetic visual percepts. Through the optimization of visual perception by image processing, the efficiency of visual prosthesis devices can be further improved to better meet the needs of prosthesis wearers. In this article, recent works on prosthetic vision centering on implementing computer vision methods are reviewed. Differences, strengths, and weaknesses of the mentioned methods are discussed. The development directions of optimizing prosthetic vision and improving methods of visual perception are analyzed. [ABSTRACT FROM AUTHOR]