Designing vehicle exteriors requires repeated refinement of key proportions and viewpoints, a process traditionally reliant on manual sketching, which is often time-consuming and inefficient in early concept stages. To accelerate the design process, we are exploring the potential of utilizing AI for ideation in these early stages. However, it remains a challenging task to control proportions and maintain a fixed perspective when generating images using AI. To address these limitations, we present RatioMorph, a controllable image generation system that enables manipulation of vehicle proportions and viewpoints when generating images by AI. RatioMorph comprises two core modules. Car2BoxNet is a depth estimation model that transforms real photographs into structured box-style depth maps that capture the geometric layout of the vehicle. Box2CarNet is a diffusion-based image generator fine-tuned to produce vehicle designs that adhere to the provided geometric conditions. Both Car2BoxNet and Box2CarNet are trained on a synthetic dataset curated through automated filtering based on geometric alignment and visual quality. Evaluated within a production-adjacent automotive design workflow, RatioMorph significantly reduced early-stage design iteration time and enabled exploratory workflows that were difficult with previous AI workflows. This work introduces a domain-specific, controllable diffusion-based generation system tailored for automotive design, enabling manipulation of vehicle viewpoint and proportion. It demonstrates strong potential to accelerate early-stage workflows and outlines a path toward industrial deployment, with phased integration into production environments currently underway.

The quality of medical images is critical for Computer-aided diagnosis (CAD) and Image-guided robotic interventions because accurate and high-quality images are required to perform each task. High resolution and Signal-to-Noise Ratio (SNR) images are required to analyze and navigate the robotic instruments to the accurate localization inside the body. However, medical images are often of substantially lower quality than clean photographic images due to various factors. In this study we focus on a post-processing based strategy for reducing the amount of noise in MRI images. We propose a method based on Denoising Diffusion Probablistic Models (DDPM), also known as diffusion models the reduce the amount of noise in the image. Specifically, a two-stage DDPM method is proposed – estimating the amount of noise and designating to the correct stage in the Marchov Chain in the reverse diffusion operation, and iteratively and gradually reducing noise by reversing the process. Our experiment was performed on an actually scanned images on a clinical MR scanner, with the reference image that were averaged to match the SNR. Our quantitative and qualitative comparison shows that our method outperforms previous methods including supervised training based on two different metrics (SSIM, PSNR). It demonstrates the effectiveness of the DDPM-based method in reducing noise in the image. Moreover, the resulting image quality achieved with the proposed approach shows that tissue sub-structures are clearer. The noise reduction performance of the proposed method for multiple adjacent slices and various contrasts was tested to show the modelś ability to reduce noise across a diverse set of imaging conditions, which is essential in real-world scenarios.

Most supervised image segmentation methods require delicate and time-consuming pixel-level labeling of building or objects, especially for small objects. In this paper, we present a weakly supervised segmentation network for aerial/satellite images, separately considering small and large objects. First, we propose a simple point labeling method for small objects, while large objects are fully labeled. Then, we present a segmentation network trained with a small object mask to separate small and large objects in the loss function. During training, we employ a memory bank to cope with the limited number of point labels. Experiments results with three public datasets demonstrate the feasibility of our approach.