High-end lenses, although offering high-quality images, suffer from both insufficient affordability and bulky design, which hamper their applications in low-budget scenarios or on low-payload platforms. A flexible scheme is to tackle the optical aberration of low-end lenses computationally. However, it is highly demanded but quite challenging to build a general model capable of handling non-stationary aberrations and covering diverse lenses, especially in a blind manner. To address this issue, we propose a universal solution by extensively utilizing the physical properties of camera lenses: (i) reducing the complexity of lens aberrations, i.e., lens-specific non-stationary blur, by warping annual-ring-shaped sub-images into rectangular stripes to transform non-uniform degenerations into a uniform one, (ii) building a low-dimensional nonnegative orthogonal representation of lens blur kernels to cover diverse lenses; (iii) designing a decoupling network to decompose the input low-quality image into several components degenerated by above kernel bases, and applying corresponding pretrained deconvolution networks to reverse the degeneration. Benefiting from the proper incorporation of lenses' physical properties and unique network design, the proposed method achieves superb imaging quality, wide applicability for various lenses, high running efficiency, and is totally free of kernel calibration. These advantages bring great potential for scenarios requiring lightweight high-quality photography.

Depth information is useful to surgeons and surgical assistance systems. However, it is a challenging task to estimate the depth of various surgical scenes based on a monocular endoscope. We propose a depth estimation approach for a monocular endoscope with a stereo matching algorithm. The monocular endoscope is moved horizontally by a robotic endoscope holder to simulate a stereo vision system. The main challenge is how to obtain a proper baseline for better depth information generation as the depth range of a surgical scene is unknown beforehand. We design a baseline evaluation and selection algorithm to search for suitable baselines for surgical scenes with different depth ranges. Experimental results show that our approach improves the average accuracy of different depth scenarios by 10.8% when the error range is 2mm.

Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.