Poor lighting conditions in the real world may lead to ill-exposure in captured images which suffer from compromised aesthetic quality and information loss for post-processing. Recent exposure correction works address this problem by learning the mapping from images of multiple exposure intensities to well-exposed images. However, it requires a large number of paired training data, which is hard to implement for certain data-inaccessible scenarios. This paper presents a highly robust exposure correction method based on self-supervised learning. Specifically, two sub-networks are designed to deal with under- and over-exposed regions in ill-exposed images respectively. This hybrid architecture enables adaptive ill-exposure correction. Then, a fusion module is employed to fuse the under-exposure corrected image and the over-exposure corrected image to obtain a well-exposed image with vivid color and clear textures. Notably, the training process is guided by histogram-equalized images with the application of histogram equalization prior (HEP), which means that the presented method only requires ill-exposed images as training data. Extensive experiments on real-world image datasets validate the robustness and superiority of this technique.
Fourier single-pixel imaging (FSI) acquisition time is tied to the number of modulations. FSI has a tradeoff between efficiency and accuracy. This work reports a mathematical analytic tool for efficient sparse FSI sampling. It is an efficient and adjustable sampling strategy to capture more information about scenes with reduced modulation times. Specifically, we first conduct the statistical importance ranking of Fourier coefficients of natural images. We design a sparse sampling strategy for FSI with a polynomially decent probability of the ranking. The sparsity of the captured Fourier spectrum can be adjusted by altering the polynomial order. We utilize a compressive sensing (CS) algorithm for sparse FSI reconstruction. From quantitative results, we have obtained the experiential rules of optimal sparsity for FSI under different noise levels and at different sampling ratios.
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