An approach to improve the quality of infrared images of vein-patterns

High-performance concealment of defective pixel clusters in infrared imagers

Defective pixel concealment is a necessary procedure in infrared image processing and is widely used. However, current approaches are mainly focused on the concealment of isolated pixels and small defective pixel clusters. Consequently, these approaches cannot meet the requirements when applied to infrared detectors with large defective pixel clusters. In this paper, we present a novel and comprehensive approach to processing the image data acquired from infrared imagers with large and small defective pixel clusters. Our approach consists of preprocessing, coarse concealment, high dynamic range enhancement, and fine concealment by generative adversarial networks. Experiments using mid-wave infrared and long-wave infrared images demonstrated that the proposed approach achieves better results than the best conventional approach, known as transforming image completion, with the peak signal-to-noise ratio and structural similarity metrics improved by 2.7063 dB (16.3%) and 0.1951 dB (34.1%), respectively.

Optimizing an LED array for an infrared illumination source using the near field for venous pattern detection

Accuracy in vein detection is important for intravenous applications such as venipuncture and other medical procedures. The near-infrared (NIR) imaging technique is used to visualize veins based on the difference of absorption of the infrared light by the oxyhemoglobin present in the blood and surrounding tissue. A problem commonly found during the visualization of veins is attributed to the source of light, saturation, low contrast, and dispersion, which are causes of inhomogeneous illumination. In this work, the design and implementation of a light source with LEDs in the range of 750-860 nm are presented. To obtain the design, a numerical simulation was made using the near-field diffraction theory to obtain homogeneous illumination at 40 cm of the sample, with a radial arrangement of three concentric LED rings. With the developed light source, an experimental scheme for the acquisition of vein patterns was implemented. Using a basic algorithm for the identification of intravenous patterns, a prototype was obtained that we believe can be used in intravenous applications and other medical procedures.

Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

Application of morphological segmentation to leaking defect detection in sewer pipelines

As one of major underground pipelines, sewerage is an important infrastructure in any modern city. The most common problem occurring in sewerage is leaking, whose position and failure level is typically identified through closed circuit television (CCTV) inspection in order to facilitate rehabilitation process. This paper proposes a novel method of computer vision, morphological segmentation based on edge detection (MSED), to assist inspectors in detecting pipeline defects in CCTV inspection images. In addition to MSED, other mathematical morphology-based image segmentation methods, including opening top-hat operation (OTHO) and closing bottom-hat operation (CBHO), were also applied to the defect detection in vitrified clay sewer pipelines. The CCTV inspection images of the sewer system in the 9th district, Taichung City, Taiwan were selected as the experimental materials. The segmentation results demonstrate that MSED and OTHO are useful for the detection of cracks and open joints, respectively, which are the typical leakage defects found in sewer pipelines.