A Novel Enhancement Algorithm Combined with Improved Fuzzy Set Theory for Low Illumination Images

A novel enhancement method of global brightness modulation and local contrast enhancement combined with the improved fuzzy set theory is proposed for color image contrast enhancement. The proposed method consists of three stages. Firstly, putting forward nonlinear global brightness mapping model adjusts dynamic range of images for luminance component V of HSV color space. Secondly, membership function is established in stages to adjust local contrast of image details nonlinearly based on fuzzy set theory. Finally, the enhanced images are transformed from HSV color space into RGB color space. The experiments further show that the proposed method has the shortest processing time, the highest AIC values, and the least NIQE values among the other four conventional methods. It has excellent effect, which can enhance the global brightness and local contrast, and advance visibility of low illumination images

Target recognitions in multiple camera CCTV using colour constancy

People tracking using colour feature in crowded scene through CCTV network have been a popular and at the same time a very difficult topic in computer vision. It is mainly because of the difficulty for the acquisition of intrinsic signatures of targets from a single view of the scene. Many factors, such as variable illumination conditions and viewing angles, will induce illusive modification of intrinsic signatures of targets. The objective of this paper is to verify if colour constancy (CC) approach really helps people tracking in CCTV network system. We have testified a number of CC algorithms together with various colour descriptors, to assess the efficiencies of people recognitions from real multi-camera i-LIDS data set via Receiver Operating Characteristics (ROC). It is found that when CC is applied together with some form of colour restoration mechanisms such as colour transfer, the recognition performance can be improved by at least a factor of two. An elementary luminance based CC coupled with a pixel based colour transfer algorithm, together with experimental results are reported in the present paper

A solution processed flexible nanocomposite electrode with efficient light extraction for organic light emitting diodes.

Highly efficient organic light emitting diodes (OLEDs) based on multiple layers of vapor evaporated small molecules, indium tin oxide transparent electrode, and glass substrate have been extensively investigated and are being commercialized. The light extraction from the exciton radiative decay is limited to less than 30% due to plasmonic quenching on the metallic cathode and the waveguide in the multi-layer sandwich structure. Here we report a flexible nanocomposite electrode comprising single-walled carbon nanotubes and silver nanowires stacked and embedded in the surface of a polymer substrate. Nanoparticles of barium strontium titanate are dispersed within the substrate to enhance light extraction efficiency. Green polymer OLED (PLEDs) fabricated on the nanocomposite electrode exhibit a maximum current efficiency of 118 cd/A at 10,000 cd/m(2) with the calculated external quantum efficiency being 38.9%. The efficiencies of white PLEDs are 46.7 cd/A and 30.5%, respectively. The devices can be bent to 3 mm radius repeatedly without significant loss of electroluminescent performance. The nanocomposite electrode could pave the way to high-efficiency flexible OLEDs with simplified device structure and low fabrication cost