Autonomous machines must self-maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. This work investigates a real-time, memory-efficient and reliable noise source estimator that combines data- and physically-based models. To this end, a DNN that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real-world noise from two camera systems, and real field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug-and-play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback-loop to approach fully reliable machines.

Shape modeling is a challenging task with many potential applications in computer vision and medical imaging. There are many shape modeling methods in the literature, each with its advantages and applications. However, many shape modeling methods have difficulties handling shapes that have missing pieces or outliers. In this regard, this paper introduces shape denoising, a fundamental problem in shape modeling that lies at the core of many computer vision and medical imaging applications and has not received enough attention in the literature. The paper introduces six types of noise that can be used to perturb shapes as well as an objective measure for the noise level and for comparing methods on their shape denoising capabilities. Finally, the paper evaluates seven methods capable of accomplishing this task, of which six are based on deep learning, including some generative models.

This tutorial shows how to model noise in a Digital Camera. While most methods just add Gaussian or even Uniform noise to an image, they are not characteristic of true image noise. True image noise is not additive or white, it depends on the image intensity level and has spatial correlations that are introduced through Demosaicing [1]. To gain a better understanding of how noise can be injected into an image, we should consider the full imaging pipeline, a diagram of this pipeline is shown below. In other words, as the light propagates through the camera, there are 2 noise sources added to it along the way.

When visibility is low at night and you turn on your smartphone camera, the video preview is full of darkness, and the visibility is even lower than what you can see. With the surging of real-time video apps, we have seen various video enhancement technologies (such as beautification or AR stickers) that make a video look better than it is. You may wonder if there is a technology that can make video look clearer than it is in low light conditions. The answer is surely a YES. And there are a few more scenarios where there are strong demands for low-light image enhancement technology as follows.

The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language ... [Show full abstract] processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability.

NexOptic Technology Corp., reports that it has made significant advancements to its cutting-edge artificial intelligence (AI) imaging solution. NexOptic's Advanced Low Light Imaging Solution (ALLIS) provides immediate solutions to problems that have plagued the imaging industry for decades. NexOptic's engineered AI drastically reduces image noise common to all imaging systems while improving performance in low light conditions. This is accomplished with NexOptic's expanding suite of patent-pending, deep learning algorithms. Some of the key benefits of ALLIS include: improved low-light performance; dramatic reduction in image noise; improved downstream applications (computational imaging, facial recognition); enhanced long-range image stabilization; major reduction in file sizes.

While modern digital cameras have made significant strides in shooting cleaner images at high ISOs, many photographers still do battle with image noise on a regular basis. Chip maker NVIDIA has just revealed a new technique, based on deep learning, that can quickly dispatch image noise. As NVIDIA explains, typical deep learning approaches have required training a neural network to recognize when a clean end state image should look like based on a series of noisy images. Armed with this information, the network can then take a fresh, noisy image and remove the noise. But NVIDIA's new technique works without needing to be feed samples of noisy images. "It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars," the researchers stated in their paper.

If manufacturers want to sustain and grow their customer bases in a competitive environment, their products need to fulfill increasingly high quality and reliability standards. Automakers, for example, now have a target defect rate for the integrated systems of less than 1 percent. That's putting pressure on the original equipment makers (OEMs) and their suppliers who have to meet these targets at the same time that products and manufacturing processes are becoming increasingly complex and featuring numerous activities that impact quality, performance, and yield. To prevent failures of components, systems, and ultimately the product, these manufacturers need reliable methods to find defects. But quality control today is, in many cases, still performed by human inspectors, which limits its reliability and efficiency.