The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. The quality of image matching is heavily dependent on image details and naturalness. Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The experiments include both dense and sparse stereo and sequential matching algorithms where the latter is considered in the very challenging visual odometry framework. We demonstrate the improvements introduced with our image mappings on well-established datasets in the literature as well as on our own experimental scenarios that include High Dynamic Range (HDR) imagery. In addition, sequential feature matching or tracking can also benefit from our approach in varying radiometric conditions. More importantly, it is generic and can be used with most sparse as well as dense stereo matching algorithms. This representation is invariant to illumination and contrast variations. The original image is transformed into a new representation using these two mappings. The phase congruency and the local weighted mean phase angle are then computed over all the scales. Frequency analysis is carried out at different scales to account for most image content. Images are mapped using their Fourier transforms which are convolved with a set of monogenic filters. In this paper, we propose a novel image representation approach to tackle illumination variations in stereo matching problems.

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