Effective method for low-light image enhancement based on the JND and OCTM models.

Low-light images always suffer from dim overall brightness, low contrast, and low dynamic ranges, thus result in image degradation. In this paper, we propose an effective method for low-light image enhancement based on the just-noticeable-difference (JND) and the optimal contrast-tone mapping (OCTM) models. First, the guided filter decomposes the original images into base and detail images. After this filtering, detail images are processed based on the visual masking model to enhance details effectively. At the same time, the brightness of base images is adjusted based on the JND and OCTM models. Finally, we propose a new method to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments have demonstrated that the proposed method not only achieves low-light image enhancement, but also outperforms state-of-the-art methods qualitatively and quantitatively.

Effective enhancement method of low-light-level images based on the guided filter and multi-scale fusion.

Aiming to solve the problem of low-light-level (LLL) images with dim overall brightness, uneven gray distribution, and low contrast, in this paper, we propose an effective LLL image enhancement method based on the guided filter and multi-scale fusion for contrast enhancement and detail preservation. First, a base image and detail image(s) are obtained by using the guided filter. After this procedure, the base image is processed by a maximum entropy-based Gamma correction to stretch the gray level distribution. Unlike the existing methods, we enhance the detail image(s) based on the guided filter kernel, which reflects the image area information. Finally, a new method is proposed to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments show that the proposed method can provide a more significant performance in enhancing contrast, preserving details, and maintaining the natural feeling of the image than the state of the art.

Adaptive method for image dynamic range adjustment and detail enhancement.

Tone mapping operators (TMOs) aim to adjust high dynamic range (HDR) images to low dynamic range (LDR) ones so that they can be displayed on conventional devices with visual information retained. Nonetheless, existing TMOs can successfully tone-map only limited types of HDR images, and the parameters need to be manually adjusted to yield the best subjective-quality tone-mapped outputs. To cope with the aforementioned issues, an adaptive parameter-free and scene-adaptive TMO for dynamic range adjusting and detail enhancing is proposed to yield a high-resolution and high-subjective-quality tone-mapped output. This method is based on detail/base layer decomposition to decompose the input HDR image into coarse detail, fine detail, and base images. After that, we adopt different strategies to process each layer to adjust the overall brightness and contrast and to retain as much scene information. Finally, a new method, to the best of our knowledge, is proposed for visualization to generate a sequence of artificial images to adjust the brightness. Experiments with numerous HDR images and state-of-the-art TMOs are conducted; the results demonstrate that the proposed method consistently produces better quality tone-mapped images than the state-of-the-art methods.

Defect detection system for silicon solar panels under all-day irradiation.

In terms of the shortcomings of defect detection based on the electroluminescence of conventional silicon solar panels, which can only be performed under darkroom conditions, a defect detection system that can work under the Sun with any irradiance in all weather is designed. The system electrifies solar panels through a modulated current source, uses high frame rate InGaAs area array detectors for image data acquisition, and transmits images via CameraLink. Using these image data as data sources, a defect display algorithm model is designed. Through experiments, it can effectively collect the defect information of solar panels within the range of 0.2 to 1300W/m2 of sunlight irradiance. Based on this, according to the relationship of the modulated phase difference between the defective points and the nondefective points found in the experiment, an enhancing algorithm for image saliency is proposed. The results show that this algorithm can reduce background interference in an effective way and improve the contrast of defects displayed under high irradiance.