Convolution neural networks (CNNs) based methods have dominated the low-light image enhancement tasks due to their outstanding performance. However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps. Meanwhile, the self-attention based global relationship aggregation methods have been widely used in computer vision, but these methods are difficult to handle high-resolution images because of the high computational complexity. To solve this problem, this paper proposes a Linear Array Self-attention (LASA) mechanism, which uses only two 2-D feature encodings to construct 3-D global weights and then refines feature maps generated by convolution layers. Based on LASA, Linear Array Network (LAN) is proposed, which is superior to the existing state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement tasks with a smaller amount of parameters. We will release the source code at Github upon the acceptance of this submission.

In optical phase shift profilometry (PSP), parallel fringe patterns are projected onto an object and the deformed fringes are captured using a digital camera. It is of particular interest in real time threedimensional (3D) modeling applications because it enables 3D reconstruction using just a few image captures. When using this approach in a real life environment, however, the noise in the captured images can greatly affect the quality of the reconstructed 3D model. In this paper, a new image enhancement algorithm based on the oriented two-dimenional dual-tree complex wavelet transform (DT-CWT) is proposed for denoising the captured fringe images. The proposed algorithm makes use of the special analytic property of DT-CWT to obtain a sparse representation of the fringe image. Based on the sparse representation, a new iterative regularization procedure is applied for enhancing the noisy fringe image. The new approach introduces an additional preprocessing step to improve the initial guess of the iterative algorithm. Compared with the traditional image enhancement techniques, the proposed algorithm achieves a further improvement of 7:2 dB on average in the signal-to-noise ratio (SNR). When applying the proposed algorithm to optical PSP, the new approach enables the reconstruction of 3D models with improved accuracy from 6 to 20 dB in the SNR over the traditional approaches if the fringe images are noisy.

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This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images.

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Conventional fringe projection profilometry methods often have difficulty in reconstructing the 3D model of objects when the fringe images have the so-called highlight regions due to strong illumination from nearby light sources. Within a highlight region, the fringe pattern is often overwhelmed by the strong reflected light. Thus, the 3D information of the object, which is originally embedded in the fringe pattern, can no longer be retrieved. In this paper, a novel inpainting algorithm is proposed to restore the fringe images in the presence of highlights. The proposed method first detects the highlight regions based on a Gaussian mixture model. Then, a geometric sketch of the missing fringes is made and used as the initial guess of an iterative regularization procedure for regenerating the missing fringes. The simulation and experimental results show that the proposed algorithm can accurately reconstruct the 3D model of objects even when their fringe images have large highlight regions. It significantly outperforms the traditional approaches in both quantitative and qualitative evaluations.