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Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed.

Mirror detection aims to discover mirror regions in images to avoid misidentifying reflected objects. Existing methods mainly mine clues from spatial domain. We observe that the frequencies inside and outside the mirror region are distinctive. Besides, the low-frequency representing the feature semantics can help to locate the mirror region, and the high-frequency representing the details can refine it. Motivated by this, we introduce frequency guidance and propose the dual domain perception progressive refinement network (DPRNet) to mine dual-domain information. Specifically, we first decouple the images into high-frequency and low-frequency components by Laplace pyramid and vision Transformer, respectively, and design the frequency interaction alignment (FIA) module to integrate frequency features to initially localize the mirror region. To handle scale variations, we propose the multi-order feature perception (MOFP) module to adaptively aggregate adjacent features with progressive and gating mechanisms. We further propose the separation-based difference fusion (SDF) module to establish associations between entities and imagings and discover the correct boundary to mine the complete mirror region. Extensive experiments show that DPRNet outperforms the state-of-the-art method by an average of 3% with only about one-fifth of the parameters and FLOPs on four datasets. Our DPRNet also achieves promising performance on remote sensing and camouflage scenarios, validating its generalization. The code is available at https://github.com/winter-flow/DPRNet.

The presence of blur is prevalent in satellite remote sensing images (RSIs), and its detrimental impact on downstream applications cannot be overlooked. Current deep learning approaches for image deblurring have gained substantial attention due to their effectiveness and fast inference speed. However, these methods often heavily rely on extensive paired training datasets and lack interpretability. Existing deblurring datasets primarily include regular scenes while remote sensing images exhibit distinct blurring mechanisms. Consequently, deep learning methods lacking prior physical knowledge can only tackle the image deblurring problem in specific scenarios, but hard to achieve satisfactory results on remote sensing images. To address these problems, it is essential to construct a remote sensing image dataset that incorporates the realistic causes of blurriness and integrate prior knowledge into the methods. In this work, we first analyze the satellite imaging system and use Zernike polynomials to approximate the optical aberrations to simulate the RSI blurring process which ensures the proposed dataset adhering solid physical principles. Moreover, we propose a novel physics-guided RSI deblurring (PGRSID) network that integrates an explicit Wiener deconvolution process in both spatial and deep feature space. This integration better leverages the physical interpretation to facilitate effective learning for the RSI deblurring network. We further incorporate denoise loss and cycle consistency loss in the objective function to facilitate the model’s learning process for RSI deblurring. Extensive experiments are conducted on both our synthetic dataset and real GF-1A/PMS data. Qualitative and quantitative experiment results highlight the effectiveness and superiority of our physics-guided deblurring network for satellite RSI.

A laser diode array side-pumped Nd:glass square rod amplifier of the dimensions 12??mm×12??mm is designed. The fluorescence is evenly distributed in the Nd:glass amplifier. When the pump power is 66.32 kW, the small signal gain increases by 3.23 times. Under the condition of a 1 Hz repetition frequency, 50 mJ of injected seed-light energy, and a 10??mm×10??mm aperture, the output energy of the laser beam can reach 1.62 J during the four-pass amplification. The output energy stability of the laser pulse is 2.94% (RMS), and the square pulse distortion is smaller than 2. The energy amplification of the injected laser beam from millijoules to joules is realized.