Raw Images Research Articles

ScTML: a pan-cancer single-cell landscape of multiple mutation types.

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

LLD-GAN: An end-to-end network for low-light image demosaicking

Demosaicking of low and ultra-low light images has wide applications in the fields of consumer electronics, security, and industrial machine vision. Denoising is a challenge in the demosaicking process. This study introduces a comprehensive end-to-end low-light demosaicking framework called LLD-GAN (Low Light Demosaicking Generative Adversarial Network), which greatly reduces the computational complexity. Our architecture employs a Wasserstein GAN framework enhanced by a gradient penalty mechanism. We have redesigned the generator based on the UNet++ network as well as its corresponding discriminator, which makes the model learning more efficient. In addition, we propose a new loss metric grounded in the principles of perceptual loss to obtain images with better visual quality. The contribution of Wasserstein GAN with gradient penalty and perceptual loss function was proved to be beneficial by our ablation experiments. For RGB images, we tested the proposed model under a wide range of low light levels, from 1/30 to 1/150 of normal light level, for 16-bit images with added noise. For actual low-light raw sensor images, the model was evaluated under three distinct lighting conditions: 1/100, 1/250, and 1/300 of normal exposure. The qualitative and quantitative comparison against advanced techniques demonstrates the validity and superiority of the LLD-GAN as a unified denoising-demosaicking tool.

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

An Open Annotated Dataset and Baseline Machine Learning Model for Segmentation of Vertebrae with Metastatic Bone Lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions. In addition to raw images, data include manual segmentations and contours, vertebral level labeling, vertebral lesion-type classifications, and patient demographic details. Our automated segmentation model uses nnU-Net, a freely available open-source framework for deep learning in healthcare imaging, and is made publicly available. This data will facilitate the development and validation of models for predicting the mechanical response to loading and the resulting risk of fractures and spinal deformity.

FIELDimagePy: A tool to estimate zonal statistics from an image, bounded by one or multiple polygons

AbstractVegetation indices have become an indispensable tool in remote sensing‐based agricultural research. A recent area of advancement in agricultural remote sensing research is in high‐throughput phenotyping, often conducted on a plot by plot basis. FIELDimageR is a tool used extensively in high‐throughput phenotyping that estimates zonal statistics of vegetation indices per plot. However, being written in R language, FIELDimageR requires high computing time. As a high‐resolution image over a large area means a large number of pixels, FIELDimageR is incapable of using high‐resolution orthomosaicked images without reducing image resolution by aggregating digital numbers of several pixels and treating them as one pixel. This research tool implements FIELDimageR in the Python language as FIELDimagePy. FIELDimagePy follows similar workflows as FIELDimageR and generates equivalent results for zonal statistics of vegetation indices per plot. FIELDimagePy is significantly and substantially faster than FIELDimageR. Computing time by FIELDimagePy are three to four times lower than computing times by FIELDimageR, even when using raw images with 16 times denser pixels. Moreover, FIELDimagePy is useful beyond plot by plot research in agriculture and capable of estimating zonal statistics of any raster bounded by any polygons. With slight modifications, FIELDimagePy can be useful for other disciplines of science, such as geophysics, geography, economics, medical sciences, among others. FIELDimagePy can be accessed from the GitHub repository: https://github.com/SumantraChatterjee/FIELDimagePy.

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25μs at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10cm−1, 150 cm−1] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Characterizing the development of gravity-driven slug flows using high-speed imaging and PIV-PLIF techniques

Although developing gravity-driven slug flow frequently occurs in oil and gas, and energy systems, its development dynamics remain underexplored; this gap, in turn, has left the underlying relationships between flow evolution and transport phenomena in these applications inadequately characterized as well. The present study experimentally investigates the spatiotemporal-spectral development of gravity-driven air–water and CO2-water slug flows in a vertical 25.4 mm ID pipe. Enhanced flow visualization techniques, utilizing high-speed imaging and particle image velocimetry-planar laser induced fluorescence (PIV-PLIF), were employed to determine the behaviors of gas and liquid phases and interactions at four positions along the pipe axis. A machine vision-based algorithm was employed to extract slug unit cell characteristics and instantaneous void fraction signals, allowing for a comprehensive statistical analysis of gas phase behavior across the flow domain. A novel algorithm was also developed to preprocess raw PIV-PLIF images, facilitating phase discrimination and noise reduction before PIV cross-correlation analyses are conducted. The results showed a logarithmic growth in the lengths of Taylor bubbles, liquid slugs, and slug unit cells along the pipe, with liquid slugs constituting nearly 60 % of slug units downstream. Taylor bubble length distributions correlated well with log-normal fits, while liquid slug and unit cell lengths transitioned from log-normal patterns upstream to near-normal distributions downstream with broader and less peaked shapes. Taylor bubble velocities and appearance frequencies of the flow structures declined exponentially along the pipe, with Taylor bubble velocities showing narrower and more peaked near-normal distributions downstream. Instantaneous void fraction signals exhibited fewer, wider peaks and troughs with reduced small-amplitude oscillations downstream. The analysis of the signals indicated a complete bubbly-to-slug transition at Z/D=30. Gas-liquid phase interactions, classified as almost-zero, weak, and strong, impacted liquid phase velocity profiles and the behavior of Taylor bubbles, with minimum stable liquid slug lengths of 8–9 D and a wake length of 1.8 D observed. Empirical correlations were developed to represent the spatiotemporal-spectral aspects of flow development, with spectral parameters, particularly liquid slug frequency, identified as the most reliable indicators of the fully developed region, predicting entrance lengths of 114.0 D and 113.4 D for air–water and CO2-water, respectively. Gas density was found to strongly influence flow characteristics and transition, accelerating the approach to the fully developed region.

A temporal (phospho-)proteomic dataset of neurotrophic receptor tyrosine kinase signalling in neuroblastoma

Neurotrophic receptor tyrosine kinases (TrkA, TrkB, TrkC), despite their homology, contribute to the clinical heterogeneity of the childhood cancer neuroblastoma. TrkA expression is associated with low-stage disease and is often seen with spontaneous tumour regression. Conversely, TrkB is present in unfavourable neuroblastomas that often harbour amplification of the MYCN oncogene. The role of TrkC is less clearly defined, although some studies suggest its association with a favourable outcome. Understanding the differences in activity of Trk receptors that drive divergent clinical phenotypes as well as the influence of MYCN amplification on downstream Trk receptor signalling remains poorly understood. Here, we present a comprehensive label-free mass spectrometry-based total proteomics and phosphoproteomics dataset (432 raw files with FragPipe search outputs; available on PRIDE with accession number PXD054441) where we identified and quantified 4,907 proteins, 16,744 phosphosites and 5,084 phosphoproteins, derived from NGF/BDNF/NT-3 treated TrkA/B/C-overexpressing neuroblastoma cells with differential MYCN status. Analysing our dataset offers valuable insights into TrkA/B/C receptor signalling in neuroblastoma and its modulation by MYCN status; and holds potential for advancing therapeutic strategies in this challenging childhood cancer.

Deep learning pipeline for automated cell profiling from cyclic imaging

Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 min, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.

Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.

DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for electron microscopy (EM), notwithstanding its ability to furnish a broad magnification range and high-resolution details for observing DNA molecules. Herein, a non-toxic, universal, and simple method is proposed that uses gold nanoparticle-tagged peptides to stain all types of naturally occurring DNA molecules, enabling their visualization under EM. This method enhances the current DNA visualization capabilities, allowing for sequence-specific, genomic-scale, and multi-conformational visualization. Importantly, an artificial intelligence (AI)-enabled pipeline for identifying DNA molecules imaged under EM is presented, followed by classification based on their size, shape, or conformation, and finally, extraction of their significant dimensional features, which to the best of authors' knowledge, has not been reported yet. This pipeline strongly improved the accuracy of obtaining crucial information such as the number and mean length of DNA molecules in a given EM image for linear DNA (salmon sperm DNA) and the circumferential length and diameter for circular DNA (M13 phage DNA), owing to its image segmentation capability. Furthermore, it remained robust to several variations in the raw EM images arising from handling during the DNA staining stage.

Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study

ObjectiveTo evaluate the performance of machine learning and then deep learning to detect a systemic scleroderma (SSc) landscape from the same set of nailfold capillaroscopy (NC) images from the French prospective multicenter observational study SCLEROCAP. MethodsNC images from the first 100 SCLEROCAP patients were analyzed to assess the performance of machine learning and then deep learning in identifying the SSc landscape, the NC images having previously been independently and consensually labeled by expert clinicians. Images were divided into a training set (70 %) and a validation set (30 %). After features extraction from the NC images, we tested six classifiers (random forests (RF), support vector machine (SVM), logistic regression (LR), light gradient boosting (LGB), extreme gradient boosting (XGB), K-nearest neighbors (KNN)) on the training set with five different combinations of the images. The performance of each classifier was evaluated by the F1 score. In the deep learning section, we tested three pre-trained models from the TIMM library (ResNet-18, DenseNet-121 and VGG-16) on raw NC images after applying image augmentation methods. ResultsWith machine learning, performance ranged from 0.60 to 0.73 for each variable, with Hu and Haralick moments being the most discriminating. Performance was highest with the RF, LGB and XGB models (F1 scores: 0.75–0.79). The highest score was obtained by combining all variables and using the LGB model (F1 score: 0.79 ± 0.05, p < 0.01). With deep learning, performance reached a minimum accuracy of 0.87. The best results were obtained with the DenseNet-121 model (accuracy 0.94 ± 0.02, F1 score 0.94 ± 0.02, AUC 0.95 ± 0.03) as compared to ResNet-18 (accuracy 0.87 ± 0.04, F1 score 0.85 ± 0.03, AUC 0.87 ± 0.04) and VGG-16 (accuracy 0.90 ± 0.03, F1 score 0.91 ± 0.02, AUC 0.91 ± 0.04). ConclusionBy using machine learning and then deep learning on the same set of labeled NC images from the SCLEROCAP study, the highest performances to detect SSc landscape were obtained with deep learning and in particular DenseNet-121. This pre-trained model could therefore be used to automatically interpret NC images in case of suspected SSc. This result nevertheless needs to be confirmed on a larger number of NC images.

Interoperable and scalable echosounder data processing with Echopype

Abstract Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype’s modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies.

Research on fresh image recognition algorithms based on machine learning

The identification of fresh images tackles issues related to accurate classification, speed and flexibility enhancement, and perhaps superior food safety evaluation. In this work, the type and freshness identification (TFI) system is based on ML (machine learning). The research suggests ML techniques for identifying various meats (pork, chicken, beef, etc) flaws and differentiating between fresh and decomposing meats to decrease labour expenses, manufacturing time, and worker effort. An efficient TFI system is suggested in this work using machine learning (ML) techniques. We gather various meat samples to effectively identify the type and freshness of the meat. Pre-processing of raw images is conducted to standardize the raw data samples. In the feature extraction process, features from the normalized data are extracted to confirm the quality of the data. The retrieved data is divided into categories for fresh meat and non-fresh meat. The suggested approach is used to evaluate TFI efficiency using a Python program. In conclusion, it was discovered that this study outperformed in improving the TFI performance

A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics

China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.

Pavement Distress Detection Based on the Staged Deep Learning Classifier Approach

Pavement crack detection is a critical task that is essential to ensure the safety of roads and highways. Accurate detection and classification of pavement cracks can help transportation agencies to identify potential maintenance needs and plan appropriate interventions. In this project, we propose a staged deep learning classifier approach for pavement crack detection to provide accurate and comprehensive information about the pavement condition. In this proposed pavement crack detection model, there are four main phases: pre-processing, feature extraction, feature selection, and crack detection. Firstly, raw images are pre-processed through median filtering and histogram equalization for noise removal and contrast enhancement. Next, features such as the improved local binary pattern, gray-level co-occurrence matrix, and gray-level run length matrix are extracted from the pre-processed images. The optimal features are selected using a hybrid optimization model, the tuna customized honey badger optimization algorithm, which combines the honey badger algorithm and tuna swarm optimization. This new hybrid optimization model will enhance the search strategy for solving optimization problems. The loss function of an augmented convolutional neural network (A-CNN) is modified to compute the root mean square error instead of the entropy-based loss function. The final detected outcomes (presence or absence of cracks) are acquired from the A-CNN.

Machine Learning Approach for Arabic Handwritten Recognition

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge Model

Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a 5% RMSE and 7% SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality.

GobletNet: Wavelet-Based High-Frequency Fusion Network for Semantic Segmentation of Electron Microscopy Images.

Semantic segmentation of electron microscopy (EM) images is crucial for nanoscale analysis. With the development of deep neural networks (DNNs), semantic segmentation of EM images has achieved remarkable success. However, current EM image segmentation models are usually extensions or adaptations of natural or biomedical models. They lack the full exploration and utilization of the intrinsic characteristics of EM images. Furthermore, they are often designed only for several specific segmentation objects and lack versatility. In this study, we quantitatively analyze the characteristics of EM images compared with those of natural and other biomedical images via the wavelet transform. To better utilize these characteristics, we design a high-frequency (HF) fusion network, GobletNet, which outperforms state-of-the-art models by a large margin in the semantic segmentation of EM images. We use the wavelet transform to generate HF images as extra inputs and use an extra encoding branch to extract HF information. Furthermore, we introduce a fusion-attention module (FAM) into GobletNet to facilitate better absorption and fusion of information from raw images and HF images. Extensive benchmarking on seven public EM datasets (EPFL, CREMI, SNEMI3D, UroCell, MitoEM, Nanowire and BetaSeg) demonstrates the effectiveness of our model. The code is available at https://github.com/Yanfeng-Zhou/GobletNet.

Reinforcement Learning with Decoupled State Representation for Robot Manipulations

Deep reinforcement learning has significantly advanced robot manipulations by providing an alternative solution for designing control strategies using raw images as direct inputs. While images offer additional environmental information, the end-to-end policy training manner (from image to action) requires simultaneous representation and task learning by the agent. This often necessitates a substantial number of interaction samples to achieve satisfactory policy performance. Previous works has attempted to address this challenge by learning a visual representation model that encodes the entire image into a low-dimensional vector before the policy training. However, since this vector contains both robot and object information, it inevitably introduces coupling within the state, which can mislead the policy training process. In this study, a novel method called Reinforcement Learning with Decoupled State Representation is proposed to effectively decouple robot and object information within the state representation. Experimental results demonstrate that the proposed method exhibits faster learning speed and achieves superior performance compared to previous methods across various robot manipulation tasks. Moreover, with only 3096 offline images, the proposed method successfully applies to real-world robot pushing tasks, which demonstrates its high practicability.

Fourier Ptychographic Coherent Anti-Stokes Raman Scattering Microscopy with Point-Scanning for Super-Resolution Imaging.

Fourier ptychography (FP) is a high resolution wide-field imaging method based on the extended aperture in the Fourier space, which is synthesized from raw images with varying illumination angles. If FP is extended to coherent nonlinear optical imaging, the resolution could be further improved due to the increase of the cutoff frequency of the synthesized coherent optical transfer function (C-OTF) with respect to the order of nonlinear optical processes. However, there is a fundamental conflict between wide-field FP and nonlinear optical imaging, whereby the nonlinear optical imaging typically requires a focused excitation laser beam with high power density. To tackle the problem, in this work, a unique point-scanning FP (PS-FP) method is presented for super-resolution nonlinear optical imaging, in which the nonlinear optical signal is obtained by using focused laser beam, while the conventional FP algorithm can still be used to retrieve the super-resolution image. PS-FP coherent anti-Stokes Raman scattering (PS-FP-CARS) imaging on a variety of samples, where a 1.8-fold expansion of the OTF is achieved experimentally for enhancing vibrational imaging. Further theoretical calculation shows that the C-OTF of PS-FP higher-order CARS (PS-FP-HO-CARS) can be expanded up to ≈4.9-fold, thereby improving the spatial resolution by ≈3-fold in comparison with conventional point-scanning CARS with under tightly focused beams. The generality of PS-FP method developed in this work can be adapted to other coherent nonlinear optical imaging modalities for super-resolution imaging in tissue and cells.