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The Degree of Linear Polarization (DoLP) is a key dimension of information in polarization imaging. The quality of polarization images is greatly affected by imaging equipment and environmental factors. Therefore, an enhancement method for the DoLP image is proposed in this paper. First, a super-resolution reconstruction algorithm based on the Detail Enhancement Guided Image Filter is proposed to solve the problem of mosaicization and pseudo-polarization information caused by the degradation of spatial resolution and super-resolution reconstruction of DoFP imaging. Second, for the problem of rapid degradation of polarization image quality caused by hazy environment, a DoLP enhancement algorithm based on multi-channel difference Signal-to-Noise Ratio is proposed to improve the visual effect of polarization images in different scenes with hazy environment. The method proposed in this paper implements super-resolution reconstruction, demosaicing, pseudo-polarization information removal, and adaptive enhancement of DoLP images in DoFP systems. [ABSTRACT FROM AUTHOR]

Because of the unique imaging mechanism of infrared (IR) sensors, IR images commonly suffer from blurred edge details, low contrast, and poor signal-to-noise ratio. A new method is proposed in this paper to enhance IR image details so that the enhanced images can effectively inhibit image noise and improve image contrast while enhancing image details. First, for the traditional guided image filter (GIF) applied to IR image enhancement is prone to halo artifacts, this paper proposes a detail enhancement guided filter (DGIF). It mainly adds the constructed edge perception and detail regulation factors to the cost function of the GIF. Then, according to the visual characteristics of human eyes, this paper applies the detail regulation factor to the detail layer enhancement, which solves the problem of amplifying image noise using fixed gain coefficient enhancement. Finally, the enhanced detail layer is directly fused with the base layer so that the enhanced image has rich detail information. We first compare the DGIF with four guided image filters and then compare the algorithm of this paper with three traditional IR image enhancement algorithms and two IR image enhancement algorithms based on the GIF on 20 IR images. The experimental results show that the DGIF has better edge-preserving and smoothing characteristics than the four guided image filters. The mean values of quantitative evaluation of information entropy, average gradient, edge intensity, figure definition, and root-mean-square contrast of the enhanced images, respectively, achieved about 0.23%, 3.4%, 4.3%, 2.1%, and 0.17% improvement over the optimal parameter. It shows that the algorithm in this paper can effectively suppress the image noise in the detail layer while enhancing the detail information, improving the image contrast, and having a better visual effect. [ABSTRACT FROM AUTHOR]