Low-light images inevitably suffer from degradation problems during the enhancement process, such as loss of detail and local overexposure or underexposure. Many existing methods target only one of these issues, leading to suboptimal results. We propose a multistage Laplacian feature fusion network (MLFFNet) capable of simultaneously mitigating both degradation difficulties. MLFFNet employs a pyramid framework that incrementally learns the degradation functions across various frequency bands, leveraging Laplacian feature maps at each stage. A key innovation of our approach is the supervised refinement module, which refines features through a dual strategy: an attention mechanism that enriches the detail capture, including edges, textures, and colors, and a residual mechanism that adjusts the luminance for a balanced exposure. The resultant enhanced image benefits from channel-wise attention, ensuring superior enhancement. Finally, the enhanced image is acquired by several channel attention blocks. Extensive experiments on various datasets indicate that our proposed MLFFNet outperforms the state-of-the-art methods both qualitatively and quantitatively.
Video super-resolution (VSR) is a typical ill-posed problem aiming to increase spatial resolution of a video. To explore interframe information between adjacent frames, optical flow and deformable convolution are commonly used for temporal dependency modeling. However, they lack the ability to use nonlocal spatial information. We propose the dual-branch feature extraction network for VSR. Our method consists of feature extraction, feature fusion and refinement, and frame reconstruction. Specifically, the feature extraction is designed to explore both local and nonlocal information of input frames. For the local feature extraction branch, we use deformable convolutions to eliminate motion between frames. For the nonlocal feature extraction branch, we employ a nonlocal module to gather relevant features. We concatenate these extracted features on channel dimension for progressive feature fusion and refinement. The frame reconstruction part is responsible for increasing spatial resolution and reconstruction. Experimental results demonstrate the effectiveness of the proposed method by extensive quantitative and qualitative evaluations in public benchmark datasets.
Underexposed images are usually low in brightness and contrast, which degrade the performance of many computer vision algorithms. To solve the problem of overexposing areas that tend to be normal while recovering dark areas in low-light image enhancement tasks, we propose an image-to-patch enhancement model and design a lightweight convolutional neural network called PatchNet. Specifically, the new enhancement model indirectly enhances the network by introducing a patch image, which preserves the incremental information from the low-light image to the normal image. The incremental information is fused with the input image to recover the dark areas while protecting the normal areas of the image. Extensive experiments on real datasets demonstrate the advantages of our method over state-of-the-art methods in subjective feeling and objective evaluation. Our method has achieved better results in restoring details and the adjustment of brightness. By comparing to other methods, our method is more efficient.
Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single image super resolution (SISR) field. However, most of existing CNN-based SR models require high computing power, which is not conducive to daily use. In addition, these algorithms need to use a large number of CNN to obtain global features. Therefore, this paper proposes an image super-resolution framework based on adaptive residual neural network, using the adaptive framework to switch between global and local reasoning for internal features in a flexible way, it can extract a large number of global features without neglecting key information, which is conducive to the comprehensiveness of residual images. After the adaptive block, SENet is added to conduct channel modeling for the extracted features, and the importance of each feature channel is automatically acquired by learning method. Then, according to this importance, useful features are promoted and those that are not useful for the current task are suppressed. In this way, with more nonlinearity, the complex correlation between channels can be better fitted, and the number of parameters and computation can be reduced, which can improve the performance of super resolution to a certain extent.
Steam reheating system is emerging as a multivariable system with steam-steam exchanger, the
strong coupling and time delay characteristics. The traditional approach for the predictive control in
power plant requires modeling based on accurate mathematical model, and some multivariate
statistical algorithm cannot avoid falling into the over-fitting, therefore these approaches is not
suitable for prediction of the reheating temperature in power plants. In this paper, we used the least
squares support vector machine (LS-SVM) regression algorithm to predict the temperature of the
steam reheating in the power plant combined with the data set of the steam reheating in a 120MW
power plant. Comparing with the existing algorithms, the result shows that the LS-SVM is a robust
and reliable tool for prediction in engineering application field.
KEYWORDS: Discrete wavelet transforms, Wavelets, Convolution, Fourier transforms, Digital signal processing, Linear filtering, Electronic filtering, Evolutionary algorithms, Algorithms, Binary data
Discrete wavelet transform (DWT) is an important tool in digital signal processing. In this paper, a new algorithm to
compute DWT is proposed: first, based on the previous work of performing discrete Fourier transform (DFT) via linear
sums of discrete moments, we introduce a multiplierless DFT by performing appropriate bit operations and shift
operations in binary system; then by convolution theorem, the computation is transformed to the computation of DFT. In
addition, a efficient systolic array is designed to implement the DWT which is a demonstration of the locality of dataflow
in the algorithms. The approach is also applicable to multi-dimensional DWT.
KEYWORDS: Fourier transforms, Binary data, Digital signal processing, Evolutionary algorithms, Bridges, Medical imaging, Real-time computing, Data processing, Information technology, Pattern recognition
Discrete Fourier transform (DFT) is an important tool in digital signal processing. We have proposed an approach to
performing DFT via linear sums of discrete moments. In this paper, based on the previous work, we present a new
method of performing fast Fourier transform without multiplications by performing appropriate bit operations and shift
operations in binary system, which can be implemented by integer additions of fixed points. The systolic implementation
is a demonstration of the locality of dataflow in the algorithms and hence it implies an easy and potential hardware/VLSI
realization. The approach is also applicable to DFT inverses.
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