SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images.

The patch ordering method for image denoising uses √n by √n overlapping image patches.

The proposed dataset for CT image denoising, which consists of 512x512 CT images and 9 training images.

We collected a new low-light raw denoising (LRD) dataset for training and benchmarking. In contrast to the SID dataset, which sets a fixed exposure time to capture long and short exposure images, we captured long and short exposure images based on the exposure value (EV). Motivated by multi-exposure image fusion, the exposure value for long exposure images was set to 0, and the exposure value for short exposure was set to the commonly used parameters -1, -2, and -3. The dataset is designed for application to low-light raw image denoising and low-light raw image synthesis. The dataset contains both indoor and outdoor scenes. For each scene instance, we first captured a long-exposure image at ISO 100 to get a noise-free reference image. Then we captured multiple short-exposure images using different ISO levels and EVs, with a 1-2 second interval between subsequent images to wait for the sensor to cool down, thus avoiding unexpected noise introduced by sensor heating.

A non-local algorithm for image denoising.

A holistic approach to cross-channel image noise modeling and its application to image denoising

We created a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as BPAE cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels.

Extreme low-light denoising (ELD) dataset that covers 10 indoor scenes and 4 camera devices from multiple brands (SonyA7S2, NikonD850, CanonEOS70D, CanonEOS700D). It has three levels (800, 1600, 3200) and two low light factors(100, 200) for noisy images, resulting in 240 (3×2×10×4) raw image pairs in total.

Quantum-Enhanced Image Denoising Market Outlook

As per our latest research, the global Quantum-Enhanced Image Denoising market size reached USD 297 million in 2024, demonstrating robust momentum driven by rapid advancements in quantum computing and its integration into image processing applications. The market is set to expand at a CAGR of 29.4% from 2025 to 2033, with the forecasted market size projected to hit USD 2.87 billion by 2033. This remarkable growth trajectory is underpinned by the increasing demand for high-fidelity imaging solutions across industries such as healthcare, aerospace, and security, where image clarity and accuracy are paramount.

One of the primary growth factors fueling the Quantum-Enhanced Image Denoising market is the escalating complexity and volume of image data generated across various sectors. The proliferation of high-resolution imaging devices and the need for precise image analysis in critical fields like medical diagnostics and satellite imaging have heightened the demand for advanced denoising solutions. Quantum computing, with its unparalleled computational power, offers a significant leap over classical methods, enabling more effective noise reduction and image restoration. This capability is particularly vital in applications where even minor image distortions can lead to erroneous interpretations or decisions, such as in radiology or remote sensing.

Another significant driver is the surge in research and development investments by both public and private entities aiming to harness quantum technologies for practical, real-world applications. Governments and leading technology firms are pouring resources into the development of quantum algorithms and machine learning models specifically tailored for image denoising tasks. These initiatives are not only advancing the state-of-the-art but are also lowering the barriers to adoption for industries that require robust and reliable image processing solutions. The growing ecosystem of quantum hardware and software providers further accelerates market growth by providing scalable, accessible platforms for deploying quantum-enhanced denoising algorithms.

Moreover, the increasing integration of artificial intelligence and quantum computing is creating new opportunities for innovation in image denoising. Quantum machine learning models are proving to be exceptionally effective in handling the vast datasets and complex patterns inherent in modern imaging applications. This synergy between AI and quantum technologies is enabling breakthroughs in noise reduction, image reconstruction, and feature extraction, which are critical for sectors like security and surveillance, industrial inspection, and autonomous vehicles. As these technologies mature and become more commercially viable, their adoption is expected to surge, driving further expansion of the Quantum-Enhanced Image Denoising market.

Regionally, North America currently leads the market, thanks to its strong technological infrastructure, significant R&D investments, and the presence of major quantum computing firms. However, Asia Pacific is rapidly emerging as a key growth region, fueled by increasing government initiatives, expanding industrial bases, and rising adoption of advanced imaging technologies in countries like China, Japan, and South Korea. Europe also holds a substantial share, driven by its robust healthcare and aerospace sectors. The Middle East & Africa and Latin America, while currently smaller in scale, are expected to witness accelerated growth as quantum technologies become more accessible and affordable in the coming years.

Technology Analysis

The technology segment of the Quantum-Enhanced Image Denoising market encompasses Quantum Machine Learning, Quantum Annealing, Quantum Algorithms, and other emerging quantum-based techniques. Quantum Machine Learning stands out as a transformative force, leveraging the unique properties of quantum bits (qubits) to process and analyze image data at unprecedented speeds. This technology enables more sophisticated denoising models that can adapt to various noise patterns and complexities, making it highly effective for applications requiring high precision. The ability to train deep learning models on quantum computers also opens up new possibilities for real-time image enhancement, which is particularly valuable in time-sensitive fields such as medical imaging and autonomous navigation.

Comparison of the proposed methods with the state-of-the-art image denoising methods in terms of structural similarity (SSIM) and feature similarity (FSIM) for a range of input noise levels σ = 10 to σ = 50.

Quantum-Enhanced Image Denoising Market Outlook

According to our latest research, the global Quantum-Enhanced Image Denoising market size reached USD 182 million in 2024, with a robust compound annual growth rate (CAGR) of 38.1% projected through the forecast period. By 2033, the market is expected to achieve a value of USD 2.78 billion, underscoring the rapid adoption of quantum technologies for advanced image processing. This surge is primarily driven by increasing demand for high-fidelity imaging across industries, coupled with the ongoing advancements in quantum computing and machine learning integration. As per our latest research, the market is experiencing significant momentum due to the convergence of quantum algorithms and artificial intelligence, which is revolutionizing the landscape of image denoising.

The primary growth factor for the Quantum-Enhanced Image Denoising market is the escalating need for superior image clarity in mission-critical applications such as medical diagnostics, satellite imagery, and industrial quality control. Traditional image denoising techniques, while effective to a certain extent, often fall short when handling high-noise environments or when tasked with preserving minute details. Quantum-enhanced approaches leverage quantum superposition and entanglement to process vast datasets in parallel, resulting in unprecedented speed and accuracy. This technological leap is particularly vital in healthcare, where early and accurate detection of anomalies in medical images can directly impact patient outcomes. Additionally, the proliferation of high-resolution imaging sensors and the exponential growth of data generated by these devices are compelling organizations to adopt quantum-enhanced solutions to maintain competitive advantage.

Another significant driver is the growing integration of quantum machine learning algorithms with conventional image processing pipelines. Quantum algorithms, such as quantum support vector machines and quantum neural networks, are demonstrating marked improvements in denoising performance, especially in low-light or high-noise scenarios. These advancements are not limited to healthcare; industries such as aerospace, defense, and manufacturing are increasingly investing in quantum-enhanced denoising to improve the accuracy of defect detection, surveillance, and remote sensing. The quantum advantage in processing speed and the ability to handle complex, multidimensional data are opening new possibilities for real-time applications, further fueling market growth.

The market is also benefitting from substantial investments by both public and private sectors in quantum technology research and development. Governments and leading technology companies are allocating significant resources to accelerate the commercialization of quantum computing, which directly supports the evolution of quantum-enhanced image denoising solutions. Collaborative initiatives between academia, industry, and government agencies are fostering innovation and driving the deployment of pilot projects across various sectors. Moreover, the increasing accessibility of cloud-based quantum computing platforms is democratizing the adoption of quantum-enhanced image denoising, enabling even small and medium-sized enterprises to leverage cutting-edge technology without substantial capital expenditure.

From a regional perspective, North America currently dominates the Quantum-Enhanced Image Denoising market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific region. The robust ecosystem of quantum technology startups, strong academic research infrastructure, and significant government funding in the United States and Canada are key contributors to North America's leadership. Meanwhile, Europe is witnessing rapid growth due to strategic collaborations and a focus on industrial automation and healthcare innovation. The Asia Pacific region, led by China, Japan, and South Korea, is emerging as a hotbed for quantum research, with increasing investments in quantum computing infrastructure and a growing demand for advanced imaging across manufacturing and automotive sectors. These regional dynamics are shaping the global competitive landscape and influencing market trajectories.

This dataset contains high-quality original images and their corresponding synthetically generated noisy variants using 7 common noise types: Gaussian, Speckle, Poisson, Multiplicative, JPEG Compression, Quantization, and Salt & Pepper. It's specifically designed to support the development, training, and benchmarking of deep learning models for image denoising, restoration, and computer vision tasks.

The noisy images were generated using Python and scikit-image, OpenCV, and NumPy, simulating realistic noise patterns that occur in real-world scenarios such as low-light imaging, compression artifacts, sensor defects, and quantization errors.

Ideal for training CNNs like U-Net, DnCNN, RIDNet, or for multi-noise classification tasks.

Each subfolder under noises/ contains synthetically altered images of the same IDs found in original/.

Set12 is a collection of 12 grayscale images of different scenes that are widely used for evaluation of image denoising methods. The size of each image is 256×256.

A New Method for Nonlocal Means Image Denoising Using Multiple Image

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

The AI image denoiser software market is experiencing robust growth, driven by the increasing demand for high-quality images across various sectors, including photography, filmmaking, and digital art. The market's expansion is fueled by advancements in artificial intelligence and machine learning algorithms that enable more efficient and effective noise reduction. Consumers and professionals alike benefit from the ability to enhance image clarity and detail without significant loss of resolution, leading to a wider adoption of these tools. While precise market sizing data is unavailable, a reasonable estimation based on the growth of related AI-powered image editing software markets suggests a current market value (2025) of approximately $300 million. Considering a projected CAGR (Compound Annual Growth Rate) of 15% over the next decade (2025-2033), the market is poised to reach approximately $1.2 billion by 2033. This growth is further propelled by the increasing availability of affordable and user-friendly AI image denoising software, making this technology accessible to a broader range of users. Several factors are contributing to market growth, including the increasing prevalence of digital photography and videography, the rise of social media platforms with high image resolution demands, and the need for high-quality imagery in professional settings. However, challenges remain. Cost constraints, particularly for high-end software solutions and sophisticated algorithms, may limit entry into the market for some users. Additionally, ensuring high-quality results consistently across different image types and noise levels remains an ongoing challenge for software developers. Continued innovation and competition among key players like AI Image Enlarger, Media.io, Fotor, and others will be crucial in driving down costs and improving the quality and accessibility of AI image denoising software, leading to further market penetration and growth.

Images often contain noise due to imperfections in various image acquisition techniques. Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in the human brain) can be clearly seen, and the subsequent image analyses are reliable. With broad usage of images in many disciplines—for example, medical science—image denoising has become an important research area. In the literature, there are many different types of image denoising techniques, most of which aim to preserve image features, such as edges and edge structures, by estimating them explicitly or implicitly. Techniques based on explicit edge detection usually require certain assumptions on the smoothness of the image intensity surface and the edge curves which are often invalid especially when the image resolution is low. Methods that are based on implicit edge detection often use multiresolution smoothing, weighted local smoothing, and so forth. For such methods, the task of determining the correct image resolution or choosing a reasonable weight function is challenging. If the edge structure of an image is complicated or the image has many details, then these methods would blur such details. This article presents a novel image denoising framework based on local clustering of image intensities and adaptive smoothing. The new denoising method can preserve complicated edge structures well even if the image resolution is low. Theoretical properties and numerical studies show that it works well in various applications.

The AI image denoiser software market is experiencing robust growth, driven by the increasing demand for high-quality images across various applications. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the proliferation of high-resolution cameras and imaging devices generates substantial amounts of noisy data, necessitating effective denoising solutions. Secondly, the advancements in artificial intelligence and deep learning algorithms are leading to the development of increasingly sophisticated and accurate denoising software. This translates to improved image quality, reduced post-processing time, and enhanced user experience. Thirdly, the rising adoption of AI-powered image editing tools across diverse sectors, including photography, filmmaking, healthcare (medical imaging), and scientific research, is boosting market demand. The market segmentation shows strong growth in cloud-based solutions due to their accessibility and scalability, while large enterprises are the primary adopters due to higher budgets and greater image processing needs. However, certain restraints are affecting market penetration. The high initial investment cost for advanced software and the need for specialized skills to operate them effectively can act as barriers, particularly for smaller businesses and individual users. Furthermore, concerns related to data privacy and security in cloud-based solutions need careful consideration. Despite these challenges, the ongoing technological innovations and the increasing awareness of the benefits of AI image denoising are expected to overcome these limitations, driving continued market expansion in the coming years. The competition is fierce, with established players like Topaz Labs and newcomers like ImageWith.AI vying for market share. The future holds significant potential for further advancements in algorithm efficiency, integration with other image editing tools, and wider accessibility, ensuring the continuous growth of this dynamic market.

Color BSD68 dataset for image denoising benchmarks is part of The Berkeley Segmentation Dataset and Benchmark. It is used for measuring image denoising algorithms performance. It contains 68 images.

AI Dental Cone-Beam Image Denoising Market Outlook

According to our latest research, the global AI Dental Cone-Beam Image Denoising market size reached USD 226.8 million in 2024, driven by a robust adoption of artificial intelligence technologies in dental imaging. The market is expected to grow at a CAGR of 17.4% from 2025 to 2033, reaching an estimated USD 765.3 million by 2033. This remarkable expansion is fueled by the increasing demand for high-precision diagnostics, the rising prevalence of dental disorders, and the need for reduced radiation exposure in dental practices. As per the latest research, the integration of AI-powered denoising solutions is rapidly transforming dental imaging workflows, offering significant improvements in both image quality and diagnostic accuracy.

One of the primary growth factors for the AI Dental Cone-Beam Image Denoising market is the continuous advancement in machine learning algorithms and deep learning frameworks. These technologies have enabled the development of highly effective denoising solutions that can significantly enhance the clarity and resolution of cone-beam computed tomography (CBCT) images. By reducing noise and artifacts, AI-driven denoising tools empower dental professionals to make more accurate diagnoses, particularly in complex cases involving bone structures, soft tissues, and dental implants. The growing body of clinical evidence supporting the efficacy of AI-based denoising is further encouraging dental practices and imaging centers to invest in these solutions, thereby fueling market growth.

Another key driver propelling the AI Dental Cone-Beam Image Denoising market is the increasing emphasis on patient safety and comfort. Traditional CBCT imaging often requires higher radiation doses to achieve clear images, which raises concerns about cumulative exposure, especially for pediatric and repeat patients. AI-powered denoising solutions address this challenge by enabling high-quality imaging at lower radiation levels. This not only ensures patient safety but also aligns with stringent regulatory guidelines and best practices in medical imaging. As awareness regarding the benefits of AI denoising continues to spread among dental professionals and patients alike, the adoption rate is expected to accelerate, contributing to sustained market expansion.

In addition to technological advancements and safety considerations, the market is also benefitting from the growing integration of digital dentistry and the expansion of dental service providers. The proliferation of dental clinics, hospitals, and diagnostic centers equipped with advanced imaging infrastructure is creating a fertile ground for the deployment of AI-based denoising solutions. Furthermore, the increasing availability of cloud-based AI platforms and the rise of tele-dentistry are making these technologies more accessible to a wider range of end-users, including those in remote and underserved regions. As the dental industry continues its digital transformation journey, the demand for AI-powered image enhancement tools is projected to remain strong.

From a regional perspective, North America currently dominates the AI Dental Cone-Beam Image Denoising market, accounting for over 37% of the global revenue in 2024, followed by Europe and Asia Pacific. The strong presence of leading dental imaging companies, favorable reimbursement policies, and a high rate of technology adoption are key factors driving the North American market. Meanwhile, Asia Pacific is emerging as the fastest-growing region, supported by rising healthcare investments, expanding dental infrastructure, and increasing awareness about advanced imaging solutions. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as dental care becomes a greater priority in these regions.

Component Analysis

The AI Dental Cone-Beam Image Denoising market is segmented by component into Software, Hardware, and Services. The so