The RAW domain image super-resolution faces two critical challenges: the physical impossibility of capturing native high-quality RAW references with a resolution-limited camera and the limitations of neural networks, including inefficient residual layer utilization and spectral bias in feature learning. This paper proposes a strategy combining physics-based imaging simulation and neural networks to jointly address these challenges. First, we develop a rapid imaging simulation system based on our proposed subgraph decomposition technology. It generates camera-specific degraded and clean RAW image pairs at multiple resolutions. Second, we design a LatentKAN network, featuring an iterative feature fusion network that extracts additional beneficial information through stage-wise supervision and a multi-layer Kolmogorov Arnold network that suppresses spectral bias via learnable activation functions. Ultimately, our strategy demonstrates significant advantages, achieving an average 0.8 dB PSNR improvement across all SR scales compared to state-of-the-art methods, thereby establishing a new paradigm for camera-specific super-resolution tasks.

To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photorealistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photorealistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging Process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-worldfoggy images.

Event cameras, offering high temporal resolution and high dynamic range, have brought a new perspective to addressing 3D reconstruction challenges in fast-motion and low-light scenarios. Most methods use the Neural Radiance Field (NeRF) for event-based photorealistic 3D reconstruction. However, these NeRF methods suffer from time-consuming training and inference, as well as limited scene-editing capabilities of implicit representations. To address these problems, we propose Event-3DGS, the first event-based reconstruction using 3D Gaussian splatting (3DGS) for synthesizing novel views freely from event streams. Technically, we first propose an event-based 3DGS framework that directly processes event data and reconstructs 3D scenes by simultaneously optimizing scenario and sensor parameters. Then, we present a high-pass filter-based photovoltage estimation module, which effectively reduces noise in event data to improve the robustness of our method in real-world scenarios. Finally, we design an event-based 3D reconstruction loss to optimize the parameters of our method for better reconstruction quality. The results show that our method outperforms state-of-the-art methods in terms of reconstruction quality on both simulated and real-world datasets. We also verify that our method can perform robust 3D reconstruction even in real-world scenarios with extreme noise, fast motion, and low-light conditions. Our code is available in https://github.com/lanpokn/Event-3DGS.

Embedded thermal sensors are very susceptible to a variety of noise sources, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of dynamic thermal management (DTM). In this paper, a smoothing filter-based Kalman prediction technique is proposed to estimate the accurate temperatures of noisy sensors. On this basis, a multi-sensor synergistic calibration algorithm is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an AMD quad-core processor in real-time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the synergistic calibration scheme can achieve an average reduction of the root-mean-square error (RMSE) by 75.9% compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 21.6%. These results clearly demonstrate that if our approach is used to perform the temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

Perceptual video coding has the potential to provide the same visual quality at a lower bit-rate, compared with the traditional objective quality based scheme. Visual saliency represents the probability of human attention over frames, and it is used for allocating coding bits or controlling visual quality. In this paper, a HEVC compliant perceptual video coding scheme is proposed based on visual saliency. At first visual saliency map is attained to indicate the distribution of saliency. Then refined distortion allocating method is performed in CU level with adaptive QP which is adjusted by the average visual saliency. Besides, a fast CU mode decision algorithm suitable for perceptual video coding in HEVC is proposed to accelerate the encoder. In the fast algorithm, the average saliency is used to estimate texture complexity and movements in videos. Experimental results show that up to 22.52% bit-rate and 43.48% encoding time can be saved by our methods with negligible perceptual quality loss.

Like the previous video coding standard, DCT and quantization are also adopted in HEVC. Compared with H.264/AVC, HEVC employs larger transform blocks, which makes the all zero block detection algorithm designed for H.264/AVC inefficient for HEVC. An efficient all zero block detection algorithm aimed at HEVC, especially for 16×16 and 32×32 transform blocks, is proposed in this paper based on frequency characteristics of DCT. By analysing the distribution of residual energy in frequency domain, Hadamard transform is used to evaluate only a part of DCT coefficients which usually consume most of energy. To make the proposed algorithm efficient in complex sequences, a sum of absolute transformed difference based method is used to evaluate the maximum value of the rest of the coefficients. Experimental results show that more than 90% all zero blocks for 4×4, 8×8 and 16×16 transform blocks and about 80% for 32×32 transform blocks can be detected by the proposed algorithm. In addition, about 50% computational complexity in DCT/quantization can be reduced with negligible loss of video quality and compression efficiency.