Super‐resolution approach to overcome physical limitations of imaging sensors: An overview

Abstract

Although the performance of CCD and CMOS imaging sensors has improved since their invention, they still have several physical limitations, such as various sources of noise, limited dynamic range, and limited spatial resolution. Besides these physical limitations, they have malfunctioning problems, such as smearing and blooming, which degrade the quality of captured images. These limitations and malfunctioning problems can be overcome, based on device physics and circuit technology. However, a signal-processingbased approach is a good alternative solution to these problems, because it may cost less and existing imaging systems can be still utilized. In a broad sense, this signal-processing-based approach can be called a super-resolution approach. The goal of this article is to introduce a super-resolution approach that overcomes the limitations of imaging sensors. To this purpose, we describe the existing limitations of imaging sensors first, and then describe the corresponding super-resolution approach.

Overall Illuminance Uniformity of IRED Lighting in Nighttime CCTV

“…In this case, the LED source is located at the origin of the coordinate axis. The luminous flux (Φ) of the LED light sources can be calculated by equation (2) as follows [9],…”

“…This is the reason for the 'white spot' (saturation of light) caused by the difference of luminous flux intensity between its center portion and outer frame [1]. Other distorted images may be caused by blooming and smearing that degrades image quality [2]. Also, most LED light patterns are circularly symmetric with non-uniform illuminance distribution.…”

A survey on super-resolution imaging

“…Super-resolution (SR) imaging [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] aims to overcome or compensate the limitation or shortcomings of the image acquisition device/system and/or possibly ill-posed acquisition conditions to produce a higher-resolution image based on a set of images that were acquired from the same scene ( Fig. 1).…”

A Single-Frame Superresolution Algorithm for Lab-on-a-Chip Lensless Microfluidic Imaging

“…In [6], SROFM (Sub-pixel Resolving OptoFluidic Microscope) first applied the super-resolution (SR) image processing, which can generate one high-resolution (HR) image from one or multiple LR images. The SR algorithm in [6] is, however, based on the multi-frame image reconstruction, which needs to capture and store a large number (40 to 100) of subpixel-shifted LR cell images [7] with high complexity for on-chip implementation. In addition, as shown in Fig.…”