Although the traditional dehazing algorithm can improve the clarity of hazy weather images, it may lead to the loss of many details and the distortion of color saturation in the process of processing. In order to overcome this defect and enhance the details of the image, a single image dehazing algorithm based on non-subsampling contourlet transform was proposed. First, the captured fog images are mapped from RGB space to HSI space, and the luminance channel map and saturation channel map are processed separately. The luminance channel image is decomposed by non-subsampling contourlet. The obtained high-frequency components are filtered by a guided filter, which can smooth the image while maintaining the edge. The obtained low-frequency components are processed by the single-scale Retinex algorithm to enhance the details of hazy areas in the image. A new luminance channel image is obtained through certain fusion rules and the inverse transformation. Then, the degradation model of the saturation component map is established. The parameters are estimated using the dark channel prior principle, and the estimated saturation map is obtained. Finally, the new luminance channel image, the estimated saturation image and the original hue channel image are inversely transformed to RGB space, resulting in a dehazed image. Experiments show that the method in this paper can solve the problem of color distortion in bright areas to a certain extent and the color saturation of the image, while keeping the overall outline structure of the image clearly and the edge details prominent. The visibility of the whole image is also improved, which is prior to the traditional detection algorithms.
Infrared and visible image fusion can obtain an integrated image containing obvious object information and high spatial resolution background information. Therefore, combining the characteristics of infrared and visible images to obtain the fused image has important research significance. In this paper, an effective fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. The method is based on the application of a modulated pulse-coupled neural network fusion (PCNN) strategy and an energy attribute fusion strategy in the NSCT domain. First, NSCT is used to decompose the input original image into low frequency sub-images and high frequency sub-images. Then, the high frequency sub-images are fused via a multi-level morphological gradient (MLMG) domain PCNN and the low frequency sub-images are fused via the energy attribute fusion strategy. Finally, the fused sub-images are reconstructed by inverse NSCT. Experimental results demonstrate that the proposed algorithm has a better fusion performance in both subjective evaluation and objective evaluation.
In order to improve the performance of low-quality noise grayscale image edge detection, using the principle that phase consistency is invariant to changes in grayscale and contrast, a noise image edge detection based on the fusion of multi-angle morphology filtering and phase consistency is proposed. The algorithm improves the defects of the previous edge detection algorithms that only rely on a single gray gradient difference or only use fixed direction weights and experimental results show that our algorithm is more accurate in noise suppression and edge detection of low-quality noise images than traditional algorithms.
Aiming at the problem of the traditional neural network for non-uniformity correction easy to cause ghosting artifacts and image blurring, an improved non-uniformity correction algorithm based on neural network is proposed. Firstly, a new fast trilateral filter is designed, which can be regarded as an edge-preserving smoothing operator. Secondly, in order to stabilize and accelerate the learning process, it adopts the self-adaptive learning rate and applies additional momentum factor to the neural network. Thirdly, in order to update the calibration parameters accurately, the local motion of different areas is judged carefully. The simulating experiments indicate that the proposed algorithm can suppress the ghosting artifacts and the image degradation. And it has better performance compared with other algorithms.
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