Conventional Image Signal Processors (ISPs) employ manually crafted designs with limited adaptability, resulting in suboptimal performance in dynamic environments for both visual quality and machine vision applications. While deep learning facilitates adaptive AI-ISPs, supervised approaches encounter domain shift limitations and substantial computational demands that impede edge deployment. This work introduces an adaptive zero-shot AI-ISP that dynamically optimizes processing pipelines without requiring paired training data. The proposed architecture implements dual specialized subnetworks for illumination estimation and denoising enhancement, operating collaboratively under Retinex theory principles to achieve boundary-aware illumination mapping and noise-resilient image restoration. Additionally, a physically constrained loss function is introduced to enhance color fidelity and noise suppression. For practical implementation, an FPGA-accelerated computing engine replaces transposed convolution with optimized bilinear interpolation, effectively eliminating artifacting while achieving superior memory efficiency through customized buffering architectures. A comprehensive evaluation demonstrates highly competitive performance, achieving a PSNR of 19.91/16.62 and an SSIM of 0.591/0.475 on LSRW-Huawei/Nikon datasets, alongside NIQE scores of 2.065/3.025 on DCIM and TM-DIED datasets. The hardware implementation attains 42.5 GOPS/W power efficiency, representing 35.4× and 7.3× improvements over conventional CPU and GPU platforms, establishing a comprehensive edge deployment solution for next-generation intelligent image processing systems.

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant reconstruction score consisting on discrimination and reconstruction loss is taken into account. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.

Images captured under practical low-light conditions typically suffer from insufficient brightness, color distortion, noise, and blur, and the enhancement process itself may further introduce overexposed highlights. This paper presents HLCNet, an HVI-guided Large-Small Convolutional Cross-Branch Network for low-light restoration. RGB inputs are transformed into the HVI space so that chromatic and intensity information can be enhanced in two complementary branches. Each branch applies LSConv to couple broad illumination context with local structural modeling, followed by SE channel recalibration and an LCA-based encoder–decoder, while a soft overexposure constraint suppresses excessive responses without hard clipping. To ensure a controlled comparison, CIDNet is reproduced in the same Tesla T4 environment, whereas the published CIDNet results and other previously reported values are explicitly marked as external references. On LOL-Blur, HLCNet raises the PSNR of the reproduced CIDNet baseline from 26.5438 dB to 27.6260 dB, increases the SSIM from 0.8839 to 0.8863, and reduces the LPIPS from 0.1224 to 0.1056. On LOL-v2 Real and Synthetic, it attains 23.843 dB and 25.991 dB PSNR, respectively. Ablation, sensitivity, qualitative, and perceptual color-space analyses indicate that HLCNet is particularly effective for low-light images containing blur and weak structural details.