Iterative Prompt Learning for Unsupervised Backlit Image Enhancement

We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the open-world CLIP prior not only aids in distinguishing between backlit and well-lit images, but also in perceiving heterogeneous regions with different luminance, facilitating the optimization of the enhancement network. Unlike high-level and image manipulation tasks, directly applying CLIP to enhancement tasks is non-trivial, owing to the difficulty in finding accurate prompts. To solve this issue, we devise a prompt learning framework that first learns an initial prompt pair by constraining the text-image similarity between the prompt (negative/positive sample) and the corresponding image (backlit image/well-lit image) in the CLIP latent space. Then, we train the enhancement network based on the text-image similarity between the enhanced result and the initial prompt pair. To further improve the accuracy of the initial prompt pair, we iteratively fine-tune the prompt learning framework to reduce the distribution gaps between the backlit images, enhanced results, and well-lit images via rank learning, boosting the enhancement performance. Our method alternates between updating the prompt learning framework and enhancement network until visually pleasing results are achieved. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of visual quality and generalization ability, without requiring any paired data.Comment: Accepted to ICCV 2023 as Oral. Project page: https://zhexinliang.github.io/CLIP_LIT_page

Senescence-associated lncRNAs indicate distinct molecular subtypes associated with prognosis and androgen response in patients with prostate cancer

Cellular senescence has been considered as a hallmark of aging. In this study, we aimed to establish two novel prognostic subtypes for prostate cancer patients using senescence-related lncRNAs. Nonnegative matrix factorization algorithm was used to identify molecular subtypes. We completed analyses using software R 3.6.3 and its suitable packages. Using SNHG1, MIAT and SNHG3, 430 patients in TCGA database were classified into two subtypes associated with biochemical recurrence (BCR)-free survival and subtype 2 was prone to BCR (HR: 19.62, p < 0.001). The similar results were observed in the GSE46602 and GSE116918. For hallmark gene set enrichment, we found that protein secretion and androgen response were highly enriched in subtype 1 and G2M checkpoint was highly enriched in subtype 2. For tumor heterogeneity and stemness, homologous recombination deficiency and tumor mutation burden were significantly higher in subtype 2 than subtype 1. The top ten genes between subtype 2 and subtype 1 were CUBN, DNAH9, PTCHD4, NOD1, ARFGEF1, HRAS, PYHIN1, ARHGEF2, MYOM1 and ITGB6 with statistical significance. In terms of immune checkpoints, only CD47 was significantly higher in subtype 1 than that in subtype 2. For the overall assessment, no significant difference was detected between two subtypes, while B cells score was significantly higher in subtype 1 than subtype 2. Overall, we found two distinct subtypes closely associated with BCR-free survival and androgen response for prostate cancer. These subtypes might facilitate future research in the field of prostate cancer

MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.Comment: ECCV 2022 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Re-mosaic Challenge Report. MIPI workshop website: http://mipi-challenge.org/. arXiv admin note: substantial text overlap with arXiv:2209.07060, arXiv:2209.07530, arXiv:2209.0705

MIPI 2023 Challenge on RGBW Remosaic: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge, including four tracks focusing on novel image sensors and imaging algorithms. This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023. In total, 81 participants were successfully registered, and 4 teams submitted results in the final testing phase. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.Comment: CVPR 2023 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Remosaic Challenge Report. Website: https://mipi-challenge.org/MIPI2023/. arXiv admin note: substantial text overlap with arXiv:2209.08471, arXiv:2209.07060, arXiv:2209.07530, arXiv:2304.1008

Boundary Output Feedback Stabilization for a Cascaded-Wave PDE-ODE System with Velocity Recirculation

This paper considers the output feedback stabilization for a cascaded-wave PDE-ODE system with velocity recirculation by boundary control. First, we choose a well-known exponentially stable system as its target system and find a backstepping transformation to design a state feedback controller for the original system. Second, we attempt to give an output feedback controller for the original system by introducing the observer. The resulting closed-loop system admits a unique solution which is proved to be exponentially stable. Finally, we give some numerical examples to prove the validity for the theoretical results