Infrared image enhancement based on adaptive non-local filter and local contrast

Abstract

High-quality infrared images urgently needed in military and civilian fields are always associated with appropriate contrast. However, the main challenge to obtain high-quality infrared images lies in enhancement of the contrast effectively without over-enhancement of the background and noise. Hereby, an effective infrared image enhancement approach is proposed and based on an adaptive non-local filter and local contrast. An input infrared image is detected by noisy adaptive detection to separate noisy pixels, which are filtered by non-local means filter to acquire denoised image. The grayscale histogram of the denoised image is divided into foreground and background based on the local minima value. The foreground part is enhanced by local contrast weighted distribution and max entropy gamma correction. The background part is processed by linear mapping to project the grayscale to an appropriate region. Finally, the enhanced infrared image is obtained by remapping the processed foreground and background histogram. Extensive experiments on public and homemade datasets demonstrate that our method achieves 46.8183 on image clarity, which expresses its superiority for infrared image enhancement.