System and Method for Low-Light Image Enhancement Using Hierarchical Adaptive Wavelet Decomposition with Cross-Scale Feature Fusion

A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.

BACKGROUND OF THE INVENTION

Field of the Art

The present invention is in the field of image processing, and more particularly is directed to the problem of low-light image enhancement.

Discussion of the State of the Art

Low-light digital images, such as those captured in challenging lighting conditions, can suffer from several disadvantages compared to images captured in well-lit conditions. One issue that is faced with low-light images is that of noise. Low-light images often have higher levels of noise, which can manifest as graininess or speckles in the image. This is due to the amplification of the sensor signal to compensate for the low light, which also amplifies the sensor's inherent noise. Another prevalent problem is loss of detail. In low-light conditions, the camera may struggle to capture fine details, leading to a loss of sharpness and clarity in the image. This can be exacerbated by some noise reduction algorithms, which may blur the image to reduce noise.

Recent advances in wavelet-based denoising have shown promise for low-light image enhancement. Traditional approaches employ fixed wavelet decomposition schemes that divide images into predetermined frequency subbands, typically using a standard four-band decomposition (LL, LH, HL, HH). These methods apply uniform processing across the entire image, regardless of local content complexity. While such approaches can reduce noise, they suffer from fundamental limitations. Fixed decomposition levels may be insufficient for highly detailed regions while being unnecessarily complex for smooth areas. This one-size-fits-all approach leads to either under-processing of complex textures or over-processing of simple regions, resulting in detail loss or computational inefficiency.

Furthermore, existing wavelet-based systems process each frequency subband independently, missing valuable correlations between different scales. Information present at one decomposition level could inform and improve processing at other levels, but current architectures lack mechanisms for such cross-scale communication. This isolation of processing pathways prevents the system from developing a holistic understanding of image structure across multiple scales. Additionally, current systems allocate equal computational resources to all image regions, regardless of their complexity or importance. This rigid allocation wastes processing power on simple areas while potentially under-serving regions that would benefit from more sophisticated analysis.

The computational demands of modern image sensors, particularly in mobile devices and real-time applications, require more intelligent processing strategies. As sensor resolutions increase and users expect instant results, the inefficiencies of fixed decomposition schemes become increasingly problematic. Processing every pixel with the same algorithmic complexity, regardless of content, creates unnecessary bottlenecks and power consumption.

What is needed is a hierarchical adaptive wavelet decomposition system that dynamically adjusts its processing depth based on local image complexity, implements cross-scale feature sharing to leverage multi-resolution correlations, and efficiently allocates computational resources where they provide the most benefit. Such a system would achieve superior enhancement quality while maintaining or improving processing efficiency compared to fixed decomposition approaches.

SUMMARY OF THE INVENTION

Accordingly, there is disclosed herein, systems and methods for low-light image enhancement utilizing hierarchical adaptive wavelet decomposition with cross-scale feature fusion. In a digital camera, under low-light conditions, the image sensor can suffer from a low signal-to-noise ratio. The result can be noisy images, as not enough photons are reaching the image sensor under the low-light conditions. Digital cameras may employ countermeasures for low-light, each with corresponding shortcomings. For example, a digital camera may enlarge the aperture of the camera to allow additional light to reach the image sensor, but enlarging the aperture can reduce the depth of field, causing at least part of the image to appear out of focus. An additional countermeasure can include increasing the exposure time. While this technique enables additional light to reach the sensor, it also increases the probability of undesired motion blur. Another option is increasing the ISO. ISO in digital photography refers to the sensitivity of the camera's sensor to light. ISO is an acronym for the International Organization for Standardization, which sets the standards for camera sensitivity ratings. In the context of photography, ISO is used to describe the sensor's sensitivity to light. A lower ISO number (e.g., ISO 100) indicates low sensitivity to light, meaning that more light is needed to properly expose the image. A lower ISO number is typically used in bright conditions to avoid overexposure and to maintain image quality. A higher ISO number (e.g., ISO 1600 or higher) indicates higher sensitivity to light, meaning that less light is needed to properly expose the image. This is useful in low-light conditions where there is not enough ambient light to properly expose the image. However, increasing the ISO also increases the amount of digital noise in the image, which can reduce image quality and create undesirable effects.

Disclosed embodiments address the aforementioned problems and shortcomings by performing hierarchical adaptive denoising on the low-light image in the Bayer domain prior to inputting the image information into the ISP (Image Signal Processing) pipeline. Embodiments utilize a recursive adaptive wavelet network (RAWN) architecture that creates a variable-depth decomposition tree, enabling more sophisticated processing of image regions based on their complexity. The system implements cross-scale feature fusion to share information between different decomposition levels, dynamically allocates specialized neural networks based on frequency characteristics, and employs an adaptive reconstruction engine that intelligently rebuilds the enhanced image. This hierarchical approach allows the system to allocate computational resources efficiently, processing complex image regions more thoroughly while applying lighter processing to simpler areas. By performing this advanced denoising prior to the ISP pipeline, the system achieves superior low-light image enhancement compared to fixed decomposition approaches.

According to a preferred embodiment, there is provided a computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that: create a plurality of subsampled subimages from a raw input image; analyze the raw input image to determine image characteristics; determine preprocessing parameters based on the image characteristics; perform a hierarchical adaptive wavelet decomposition process on each subimage from the plurality of subimages using the determined preprocessing parameters to generate a variable-depth decomposition tree comprising a plurality of frequency domain nodes; apply feature fusion between nodes at different decomposition levels within the decomposition tree to generate multi-scale feature representations; dynamically allocate neural networks from a network pool to process the frequency domain nodes based on node characteristics; provide outputs of the dynamically allocated neural networks to a reconstruction engine configured to traverse the decomposition tree; and provide an output of the reconstruction engine to an image signal processing pipeline.

According to an aspect of an embodiment, the software instructions create the plurality of subsampled subimages from a Bayer raw input image.

According to an aspect of an embodiment, the hierarchical adaptive wavelet decomposition process recursively decomposes frequency domain nodes based on complexity metrics exceeding an adaptive threshold.

According to an aspect of an embodiment, the cross-scale feature fusion implements attention mechanisms between parent nodes and descendant nodes to create bidirectional feature pathways across decomposition levels.

According to an aspect of an embodiment, dynamically allocating neural networks comprises selecting network architectures based on frequency characteristics of decomposition nodes and sharing weights between similar nodes.

According to an aspect of an embodiment, each decomposition node includes a gate network that determines whether to further decompose the node and selects an optimal wavelet type for decomposition.

According to another preferred embodiment, there is provided a method for image enhancement, comprising: creating a plurality of subsampled subimages from a raw input image; analyzing the raw input image to determine image characteristics; determining preprocessing parameters based on the image characteristics; performing a hierarchical adaptive wavelet decomposition process on each subimage from the plurality of subimages using the determined preprocessing parameters to generate a variable-depth decomposition tree comprising a plurality of frequency domain nodes; applying cross-scale feature fusion between nodes at different decomposition levels within the decomposition tree; dynamically allocating neural networks from a network pool to process selected nodes from the decomposition tree; providing outputs of the dynamically allocated neural networks to an adaptive reconstruction engine; and providing an output of the adaptive reconstruction engine to an image signal processing pipeline.

According to an aspect of an embodiment, creating the plurality of subsampled subimages comprises processing a Bayer raw input image.

According to an aspect of an embodiment, performing the hierarchical adaptive wavelet decomposition process comprises recursively decomposing frequency domain nodes based on complexity metrics exceeding an adaptive threshold.

According to an aspect of an embodiment, applying cross-scale feature fusion comprises implementing attention mechanisms between parent nodes and descendant nodes to create bidirectional feature pathways across decomposition levels.

According to an aspect of an embodiment, dynamically allocating neural networks comprises selecting network architectures based on frequency characteristics of decomposition nodes and sharing weights between similar nodes.

According to an aspect of an embodiment, the method further comprises determining at each decomposition node whether to further decompose the node and selecting an optimal wavelet type for decomposition using a gate network.

The drawings are not necessarily to scale. The drawings are merely schematic representations, not intended to portray specific parameters of the disclosed embodiments. The drawings are intended to depict only typical embodiments of the invention, and therefore should not be considered as limiting in scope.

DETAILED DESCRIPTION OF THE INVENTION

Low-light digital images can be difficult to enhance. Underexposed images often have a limited dynamic range, meaning there is less contrast between the darkest and lightest areas of the image. This can result in a flat or dull appearance. Image signal processing (ISP) techniques, such as increasing the brightness of an underexposed image during the ISP pipeline can lead to an increase in digital noise, particularly in the darker areas. This can result in a grainy or speckled appearance, reducing image quality. Furthermore, underexposed areas of an image may lack detail and appear as solid black areas, especially in shadowed regions. This can result in a loss of important information and reduce the overall quality of the image.

Disclosed embodiments address the aforementioned issues with a hierarchical adaptive approach that performs intelligent denoising of the input image prior to input to the ISP pipeline. Images in a Bayer RGBG format are subject to a recursive adaptive wavelet decomposition process, creating a variable-depth decomposition tree where different image regions can be processed at different levels of detail based on their complexity. This hierarchical approach represents a significant advancement over fixed decomposition schemes. In one or more embodiments, the system may be implemented on embedded processors, mobile SoCs, or GPU-accelerated compute platforms. Resource-aware features such as partial network execution, adaptive depth control, and priority-based scheduling enable real-time performance under power and memory constraints typical of smartphone or surveillance imaging systems.

While described primarily in the context of low-light enhancement, the disclosed hierarchical processing architecture may be adapted for other image enhancement tasks, including dehazing, super-resolution, demosaicing, and HDR reconstruction. The adaptive decomposition and cross-scale fusion mechanisms are broadly applicable to multi-resolution visual inference problems.

The hierarchical decomposition process begins with the same image analysis as described in the base patent, determining characteristics such as overall brightness, contrast levels, noise estimation, and detail complexity. These characteristics inform preprocessing parameters that guide the subsequent processing. However, unlike fixed decomposition approaches, the hierarchical system can make localized decisions about processing depth.

At each node in the decomposition tree, a lightweight gate network analyzes the frequency domain content to determine whether further decomposition would be beneficial. This gate network considers multiple factors including the entropy of the subband, edge density within the region, texture coherence metrics, and local variance statistics. Based on these metrics, the gate network outputs three key decisions: whether to decompose the node further, which wavelet type would be optimal for that specific decomposition, and what priority to assign for computational resource allocation. The gate network may be implemented as a three-layer convolutional neural network (CNN) with kernel sizes of 3×3, Leaky ReLU activation functions, and a softmax output layer that simultaneously generates decomposition decisions, wavelet type recommendations, and resource priorities. In one embodiment, the adaptive threshold used to determine whether to further decompose a node may range from 0.3 to 0.7, depending on global brightness and decomposition depth. Specialized denoising networks in the dynamic pool may include shallow U-Nets for smooth regions, deeper residual CNNs for high-frequency textures, and asymmetric convolutional blocks for edge regions. Network depth may vary between 4 to 12 layers, and weight sharing is applied when frequency statistics of two nodes fall within a predefined similarity threshold (e.g., cosine similarity>0.9). These architectural details reflect preferred implementations but may vary depending on target application constraints such as real-time performance or memory footprint.

The recursive nature of the decomposition allows the system to adapt to local image characteristics. For example, a region containing fine textures or complex patterns may be decomposed to greater depths, creating more frequency subbands for detailed analysis. Conversely, smooth regions with minimal detail may terminate decomposition early, conserving computational resources. This adaptive depth can vary across the image, with some branches of the decomposition tree extending to five or six levels while others stop at two or three.

A key innovation in the disclosed embodiments is the implementation of cross-scale feature fusion. Traditional wavelet processing treats each frequency subband independently, missing valuable correlations between scales. The hierarchical system implements attention mechanisms that allow information to flow bidirectionally between parent and child nodes in the decomposition tree. This creates feature highways where fine-scale details can inform coarse-scale processing and vice versa.

A cross-scale attention mechanism operates by computing attention weights between features at different scales. For each node in the decomposition tree, the system calculates how relevant the information from other nodes might be, considering both their frequency relationships and spatial correspondence. These attention weights are learned during training and allow the system to discover complex multi-scale dependencies in the image data. In one or more embodiments, the neural network components disclosed herein—including the gate network, cross-scale attention modules, specialized denoising networks, and adaptive reconstruction engine—are trained using supervised learning on datasets containing paired low-light input images and high-quality ground truth references. Training may involve datasets such as the LOL (Low-Light) dataset, SID (See-in-the-Dark), or proprietary low-light datasets curated to include a range of exposure levels, noise patterns, and scene complexities. Loss functions may include mean squared error (MSE), perceptual loss using pretrained feature extractors (e.g., VGG), and regularization terms to preserve spatial coherence and feature consistency. Optimization may be performed using Adam with learning rates in the range of 1 e-4 to 1 e-5 and batch sizes adapted to hardware capacity. Network parameters, including attention weights, blending coefficients, and reconstruction filters, are iteratively updated during training and stored for inference at runtime.

The dynamic allocation of neural networks represents another significant advancement. Rather than employing a fixed set of networks, the system maintains a pool of specialized network architectures optimized for different types of frequency content. When a new node is created in the decomposition tree, the system analyzes its characteristics and selects or spawns an appropriate network from the pool. Networks processing similar types of content can share weights, reducing memory requirements while maintaining processing quality.

The network pool includes architectures specialized for different scenarios. Networks designed for low-frequency, smooth regions may be shallower with fewer parameters, focusing on gentle denoising and contrast enhancement. Networks for high-frequency detail regions may be deeper with specialized layers for preserving fine textures while removing noise. Edge-oriented networks may include asymmetric convolutions optimized for directional features.

The adaptive reconstruction engine represents a sophisticated approach to rebuilding the enhanced image from the processed decomposition tree. Unlike simple inverse wavelet transforms, the reconstruction engine employs learned filters that can adapt based on the content being reconstructed. The engine traverses the decomposition tree in reverse, but rather than following a fixed path, it uses a priority queue based on the estimated impact of each node on the final image quality.

During reconstruction, the system applies multi-scale residual learning to capture details that may have been altered during decomposition and processing. The reconstruction engine maintains spatial correspondence across scales and implements overlapping window processing with learned blending weights to minimize boundary artifacts. This ensures smooth transitions between regions processed at different depths.

The system also implements a quality feedback mechanism where local reconstruction quality is assessed and used to inform processing decisions. This feedback can trigger reprocessing of specific branches if the quality metrics indicate suboptimal results. In one or more embodiments, this creates an iterative refinement process that continues until quality targets are met or computational budgets are exhausted. Terms such as “optimal wavelet type” and “quality threshold” refer to objectively measurable properties derived from statistical analysis and perceptual modeling. A wavelet type is considered optimal for a given node when it minimizes residual energy post-decomposition or maximizes local sparsity, as evaluated over a representative validation set. Reconstruction quality may be quantified using metrics such as Structural Similarity Index (SSIM>0.85), peak signal-to-noise ratio (PSNR>25 dB), or local entropy consistency. If these metrics fall below application-specific thresholds, the system may trigger refinement steps that selectively reprocess subtrees within the decomposition hierarchy. These quantitative thresholds may be tuned during development to reflect user preferences or display requirements across different platforms.

For real-time applications, the system can operate in a progressive mode where a quick initial result is generated using simplified processing, followed by iterative refinement as computational resources allow. This enables responsive user experiences while still achieving high-quality results when time permits. In the event of processing resource constraints or incomplete data, the system may fall back to simplified processing pathways, such as single-level decomposition with default network assignment, ensuring graceful degradation of output quality without system failure.

The hierarchical adaptive architecture provides several advantages over fixed decomposition approaches. By processing only to the depth necessary for each image region, the system can achieve better quality with lower average computational cost. The cross-scale information sharing enables more coherent enhancement that preserves image structure across scales. The dynamic network allocation ensures that specialized processing is applied where needed without waste.

Digital photography plays a significant role in today's society, influencing various aspects of our lives, including communication, entertainment, documentation, and art. The enhanced low-light capabilities provided by the disclosed embodiments expand the utility of digital photography in challenging conditions, enabling capture of important moments and details that would otherwise be lost to darkness and noise.

The hierarchical adaptive preprocessing integrates seamlessly with existing ISP pipelines, requiring no modifications to downstream processing. The system outputs a denoised image in the same format expected by the ISP, maintaining compatibility while providing superior input quality. This allows the ISP pipeline to achieve improved results in terms of extracting details from low-light images, as it operates on cleaner, more information-rich data. Certain parameters, such as the aggressiveness of decomposition, denoising strength, or reconstruction sharpness, may be adjusted via configuration settings or user preferences. These may control threshold values, wavelet family weighting, or residual blending intensity to tailor the enhancement process for specific application domains.

By implementing this hierarchical adaptive approach, the disclosed embodiments can provide superior low-light image enhancement across a broader range of scenarios than fixed decomposition methods. The system's ability to adapt its processing complexity to match image content ensures optimal quality while maintaining efficiency, making it particularly valuable for modern imaging applications where both quality and performance are critical.

Definitions

The term “bit” refers to the smallest unit of information that can be stored or transmitted. It is in the form of a binary digit (either 0 or 1). In terms of hardware, the bit is represented as an electrical signal that is either off (representing 0) or on (representing 1).

The term “pixel” refers to the smallest controllable element of a digital image. It is a single point in a raster image, which is a grid of individual pixels that together form an image. Each pixel has its own color and brightness value, and when combined with other pixels, they create the visual representation of an image on a display device such as a computer monitor or a smartphone screen.

The term “neural network” refers to a computer system modeled after the network of neurons found in a human brain. The neural network is composed of interconnected nodes, called artificial neurons or units, that work together to process complex information.

The term “signal-to-noise ratio” (SNR) is a measure used in signal processing to quantify the ratio of the strength of a signal to the strength of background noise that affects the signal. A higher SNR indicates that the signal is stronger relative to the noise, which generally means that the signal is clearer and easier to detect or interpret. SNR may be expressed in decibels (dB) and is calculated as the ratio of the power of the signal to the power of the noise

The term “Bayer RGBG format” refers to a color filter array for arranging RGB color filters on a square grid of photosensors, with green filters at opposing corners of each 2×2 unit cell, and red and blue filters in the other two positions.

The term “wavelet decomposition” refers to a method of breaking down a signal (in this case, an image) into a set of wavelets of different scales and positions, allowing for analysis of both frequency and spatial information simultaneously.

The term “frequency domain subimage” refers to an image representation that results from wavelet decomposition, where the image is divided into different frequency components.

The term “adaptive wavelet decomposition” refers to a process where the parameters of wavelet decomposition (such as wavelet type and decomposition level) are dynamically adjusted based on the characteristics of the input image.

The term “Image Signal Processing (ISP) pipeline” refers to a series of processing steps applied to raw image data from a camera sensor to produce a final, displayable image.

The term “low-light image” refers to an image captured in conditions with insufficient illumination, typically resulting in high noise levels and loss of detail.

The term “neural network” refers to a computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) that process and transmit information.

The term “Haar wavelet” refers to a sequence of rescaled “square-shaped” functions which together form a wavelet family or basis.

The term “Daubechies wavelet” refers to a family of orthogonal wavelets characterized by a maximal number of vanishing moments for some given support.

The term “Coiflet wavelet” refers to a discrete wavelet designed by Ingrid Daubechies to have scaling functions with vanishing moments.

As used herein, the terms “processing node” and “frequency domain node” refer to elements within the decomposition tree structure generated by hierarchical decomposition controller. Each node represents a localized frequency component of the input image and is subject to further decomposition, feature fusion, and neural network processing. These terms are used interchangeably throughout this disclosure to describe image regions in the wavelet-transformed domain.

Conceptual Architecture of Low Light Image Enhancement

FIG. 1 is a block diagram illustrating components for image enhancement utilizing a denoising preprocessing module, according to an embodiment. An image enhancement application 110 exists that comprises at least a denoising preprocessing module 112, neural network module 114, and inverse wavelet module 116. The image enhancement application has connections to receive raw image source data 120, and output data to the image signal processing (ISP) pipeline 160. The raw image source data 120 can include image source data in a Bayer format. A Bayer raw image is a type of image format that may be used in digital cameras and other imaging devices. The Bayer format may include images comprising multiple sets of four pixels. Each set includes a red pixel, a blue pixel, and two green pixels. This arrangement is based on the fact that the human eye is more sensitive to green light than to red or blue. One or more embodiments may utilize other formats as part of raw image source data 120, instead of, or in addition to, data in a Bayer format. The raw image data can include bitmaps, tagged image file format (TIFF), and/or other raw formats such as NEF, ARW, RAF, RW2, and/or ORF formats.

The denoising preprocessing module 112 can include functions and/or instructions for separating an image into low and high frequency components via digital filtering techniques, such as two-dimensional Fast Fourier Transforms (FFT), wavelet decomposition, and/or other suitable techniques. A combination of low-pass filtering, high-pass filtering, bandpass filtering, and/or notch filtering may be used to create multiple frequency domain subimages.

The inverse wavelet module 116 accepts wavelet coefficients from a previous wavelet decomposition and reconstructs an original signal based on the coefficients. In one or more embodiments, a threshold may be used as a criterion for processing the wavelet coefficients in order to remove noise or small details, which can help in denoising the signal or image. The thresholded coefficients can be used to create a denoised version of the original signal or image using an inverse wavelet transform. The denoised image 150 is a denoised version of an image from the raw image source data 120. Reconstructed image 150 is input to image signal processing (ISP) pipeline 160. The ISP can include one or more processors and/or specialized circuits for processing the denoised image 150. The ISP pipeline can include hardware and software for demosaicing, color correction, sharpening, tone mapping, and/or other functions. By denoising the image prior to inputting the image into the ISP pipeline 160, disclosed embodiments can achieve improved enhancement of low-light images.

FIG. 2 is a block diagram illustrating an exemplary system architecture for image enhancement, including an adaptive denoising preprocessing stage 202, according to an embodiment. A raw image 210 is converted to a subsampled image 212 in a Bayer RGBG format, indicating an arrangement where a set of pixels includes a red pixel, a blue pixel, and two green pixels. The green pixels may be oriented diagonally from each other in one or more embodiments.

The subsampled image is then input to image analysis subsystem 213, which analyzes the image to determine its characteristics such as overall brightness, contrast levels, and noise estimation. The image analysis subsystem employs a suite of sophisticated algorithms to quantify various image properties. For overall brightness, it performs a weighted histogram analysis, calculating the average pixel intensity with higher weights given to mid-range values to avoid skew from extreme outliers. The resulting brightness score is normalized to a range of 0-1. Contrast levels are measured using both global and local approaches. Global contrast is computed using the root mean square (RMS) contrast method, while local contrast is assessed using a sliding window approach with Michelson contrast calculation. The final contrast metric combines these measures, weighted based on empirically determined importance factors.

Noise estimation utilizes a wavelet-based technique, analyzing the high-frequency subbands of a discrete wavelet transform. The median absolute deviation (MAD) of the wavelet coefficients in these subbands provides a robust estimate of noise levels, which is then scaled to account for the specific characteristics of the imaging sensor. To quantify detail complexity, the subsystem applies Sobel edge detection to identify edge pixels, with the percentage of edge pixels providing a measure of image complexity. Additionally, a 2D Fourier transform is performed, and the energy distribution across different frequency bands is analyzed to further characterize image detail. The results of these analyses are combined into a feature vector, representing the image's characteristics in a multi-dimensional space. The results of this analysis are passed to an adaptation control subsystem 214, which determines the optimal preprocessing parameters based on the image characteristics.

Adaptation control subsystem 214 employs a hybrid approach to determine the optimal preprocessing parameters. At its core is a gradient boosting decision tree (GBDT) model, trained on a diverse dataset of low-light images and their optimal preprocessing parameters. This model takes the feature vector as input and outputs initial estimates for the preprocessing parameters. These initial estimates are then refined by a rule-based system that applies domain-specific knowledge. This subsystem 214 includes rules based on established image processing principles and empirical observations from extensive testing. The preprocessing parameters include the wavelet decomposition level, wavelet type selection, thresholding parameters, and subband weighting factors. The decomposition level is adjusted based on the detail complexity and noise levels, with higher complexity and lower noise typically warranting more decomposition levels. For wavelet type selection, the system chooses between Haar, Daubechies (Db4), and Coiflet (Coif3) wavelets. Haar wavelets are selected for images with sharp transitions, Db4 for smoother images with moderate detail, and Coif3 for images with fine, complex textures. Thresholding parameters, which control the denoising strength in each subband, are primarily influenced by the estimated noise level and contrast of the image. Higher noise levels result in more aggressive thresholding, while higher contrast allows for more conservative thresholding to preserve details. Subband weighting factors determine the relative importance of each frequency subband in the reconstruction process. They are adjusted based on the image's brightness and detail complexity, with darker images with high complexity receiving higher weights for high-frequency subbands to preserve details.

Adaptive wavelet decomposition subsystem 215 creates multiple frequency domain subimages. The adaptive wavelet decomposition process incorporates several key features to optimize its performance for various types of low-light images. The system dynamically selects the most appropriate wavelet type based on the image characteristics determined by the image analysis subsystem. The selection criteria include edge characteristics, texture complexity, and the presence of smooth gradients. For images with sharp, well-defined edges, as determined by edge detection algorithms, the system preferentially selects Haar wavelets due to their ability to preserve sharp transitions. Images with complex textures, identified through frequency analysis, are processed using Daubechies (Db4) wavelets, which offer a good balance between frequency localization and smoothness. For images dominated by smooth intensity transitions, the system opts for Coiflet (Coif3) wavelets, which provide higher-order vanishing moments and better handling of polynomial behavior in the image. The selection is made using a weighted scoring system that considers these factors, with the highest-scoring wavelet type being chosen for the decomposition.

The number of subimages created can also vary based on the determined preprocessing parameters. Typically, four subimages are created via high-pass and low-pass filtering, denoted as LL, LH, HL, and HH. Each image represents a different frequency range. In embodiments, LL represents the lowest frequency range, HH represents the highest frequency range, and LH and HL represent intermediate ranges, where HL represents higher frequencies than the LH range.Each frequency domain subimage is then input to a corresponding neural network (216, 218, 220, 222). These neural networks are specifically designed and trained for denoising tasks in different frequency ranges. Each network employs a convolutional architecture, which is particularly effective for image processing tasks. The networks include multiple convolutional layers, residual blocks for improved gradient flow, and Squeeze-and-Excitation blocks to adaptively recalibrate channel-wise feature responses.

A key feature of these neural networks is the use of a Leaky ReLU activation function, which helps prevent the “dying ReLU” problem and allows for more nuanced handling of negative values, which can be important in the context of frequency domain representations. The networks are trained on a large dataset of low-light images paired with their corresponding well-lit, noise-free ground truth images. This dataset includes a wide variety of scenes and lighting conditions to ensure robust performance.

The training process involves minimizing a loss function that combines mean squared error (for overall reconstruction accuracy) with perceptual loss terms (to preserve important image features and textures). The networks are trained using a combination of synthetic low-light data (created by artificially darkening and adding noise to clean images) and real-world low-light photographs paired with long-exposure ground truth images. This diverse training data helps the networks generalize well to various real-world low-light scenarios.

The LL frequency domain subimage is input to neural network 216, the LH frequency domain subimage to neural network 218, the HL frequency domain subimage to neural network 220, and the HH frequency domain subimage to neural network 222. Each network is specialized for its specific frequency range, allowing for optimal denoising performance across different scales and orientations of image features.

The processed frequency domain subimages from these neural networks are then input to inverse wavelet module 224, which reconstructs the denoised image. This denoised output is then fed into an image signal processing (ISP) pipeline 226. The ISP pipeline 226 can then operate on this cleaner, denoised input image, applying further enhancements and adjustments to produce the final enhanced low-light image 228.

The adaptive nature of this preprocessing stage, combined with the specialized neural networks, allows the system to optimize its denoising process for each specific input image, resulting in improved performance across a wide range of low-light conditions. This approach is particularly effective because it combines the power of wavelet analysis for separating different scales of image features with the learning capabilities of neural networks, all within an adaptive framework that can adjust to the specific characteristics of each input image.

In a non-limiting use case example of this system, consider a scenario where a user captures a photograph of a city street at night using a smartphone camera. The resulting image is underexposed due to the low-light conditions, with visible noise and loss of detail in shadow areas. As the raw image 210 in Bayer RGBG format enters the system, the image analysis subsystem 213 examines it, determining that it has very low overall brightness, moderate global contrast with low local contrast in shadow areas, high noise levels (particularly in darker regions), and moderate detail complexity with sharp edges from street lights and building outlines but low detail in shadowed areas. Based on this analysis, the adaptation control subsystem 214 sets the preprocessing parameters: a 3-level wavelet decomposition using Daubechies (Db4) wavelets, aggressive thresholding for high-frequency subbands to reduce noise, and higher weights assigned to mid-frequency subbands to preserve edge details while suppressing noise.

The image then undergoes adaptive wavelet decomposition in subsystem 215 using these parameters, resulting in multiple frequency domain subimages. Each of these subimages is processed by its corresponding neural network (216, 218, 220, 222): the low-frequency (LL) subimage is processed by network 216 to enhance overall brightness and contrast, the mid-frequency (LH and HL) subimages are enhanced by networks 218 and 220 for edge details and local contrast, and the high-frequency (HH) subimage undergoes more aggressive denoising in network 222. Following this, the processed subimages are recombined using the inverse wavelet module 224, resulting in a cleaner, more detailed image. This reconstructed image then passes through the ISP pipeline 226 for final adjustments, including color correction to account for the nighttime lighting conditions, further contrast enhancement to bring out details in shadow areas, and sharpening to accentuate fine details revealed by the denoising process.

The final output 228 is an enhanced version of the nighttime street scene with significantly improved overall brightness, revealing details that were previously hidden in shadows. Noise is substantially reduced, particularly in darker areas of the image, while edge details of buildings, street signs, and other urban features are more clearly defined. Importantly, despite the significant enhancement, the image maintains a natural look, avoiding an over-processed appearance. This use case demonstrates how the system's adaptive nature allows it to tailor its processing to the specific challenges of a nighttime urban scene, effectively balancing noise reduction, detail enhancement, and overall image improvement.

FIG. 3 is a diagram indicating a wavelet transform architecture, according to an embodiment. In embodiments, a Bayer raw image 302 is input to a filter 304, that produces a low frequency subimage and a high frequency subimage. In one or more embodiments, creating subsampled subimages from the raw input image comprises inputting a raw image in a Bayer raw image format. The low frequency subimage is input to downsampling module 310, and the high frequency subimage is input to downsampling module 312. In one or more embodiments, the downsampling performed by the downsampling modules can include a process of reducing the resolution of an image or subimage by removing pixels. In embodiments, the downsampling modules downsample by two. In one or more embodiments, the wavelet decomposition process further comprises downsampling. The output of downsampling module 310 is input to filter 314, where the filter 314 produces a low frequency subimage and a high frequency subimage. A similar process occurs with the output of downsampling module 312, which is input to filter 316, where the filter 316 produces a low frequency subimage and a high frequency subimage. These images are again downsampled using respective downsampling modules 318, 320, 332, and 334. The resulting output are the frequency domain subimages, indicated as LL 322, LH 324, HL 336, and HH 338. In some embodiments, the filtering may be along rows of an image, columns of an image, diagonally, and/or other suitable technique. Embodiments can include processing rows of image data, followed by processing columns of image data.

FIG. 4 is a diagram indicating a neural network architecture, according to an embodiment. The neural network architecture can include a convolutional layer 402, the output of which feeds into residual block 404, which then feeds into residual block 406. The output of residual block 406 can feed into the output of SE (Squeeze-and-Excitation) block 407. The SE block includes two main operations: squeeze and excitation. The squeeze operation involves reducing the spatial dimensions of the input feature maps to a single global value per channel. In embodiments, the squeeze operation is performed using global average pooling, which computes the average value of each channel across all spatial locations. The result is a 1D tensor with the same number of channels as the input. In the excitation operation, the 1D tensor obtained from the squeeze operation is passed through two fully connected (FC) layers with a non-linear activation function (such as the ReLU) in between. The first FC layer reduces the dimensionality of the tensor, while the second FC layer expands it back to the original number of channels. In embodiments, a sigmoid or softmax activation function is applied at the end to generate a channel-wise attention vector.

In embodiments, the output of the excitation operation is multiplied element-wise with the input feature maps to recalibrate them. This operation scales the feature maps by the importance assigned to each channel by the excitation operation. By incorporating the SE block into disclosed embodiments, the network can learn to selectively emphasize informative features and suppress irrelevant ones for the purposes of denoising an image, leading to improved accuracy and efficiency in enhancement of low-light images.

The output of the SE block 407 is input to another convolutional layer 408. The network continues to combiner 421 where the output of the convolutional layer 408 is combined with the output of convolutional layer 402, and is provided as input to convolutional layer 420, followed by shuffle layer 422, and additional convolutional layer 424. The neural network architecture of FIG. 4 is exemplary, and other embodiments may have more, fewer, and/or different components. For example, while two residual blocks are shown in FIG. 4, other embodiments may have more or fewer residual blocks.

In embodiments, the shuffle layer can be used to introduce some form of permutation or rearrangement into the data flow of a neural network, which can help improve the network's ability to learn complex patterns and relationships in the data for the purposes of denoising an image. In embodiments, the residual blocks can serve to address the problem of vanishing gradients in deep networks by introducing skip connections that allow the gradient to flow more directly through the network.

FIG. 5 is a diagram indicating additional details of the neural network architecture shown in FIG. 4, according to an embodiment. In particular, FIG. 5 shows additional details of a residual block such as shown at 404 in FIG. 4. The residual block includes a convolutional block 502. The convolutional block can include one or more convolutional layers. In embodiments, each convolutional layer/block includes a set of learnable filters (also known as kernels) are applied to the input data. Each filter is convolved with the input data to produce a feature map, which highlights the presence of particular patterns or features in the input. The convolution operation involves sliding the filter over the input data, performing element-wise multiplication and summing the results to produce a single value in the output feature map. The output of convolutional block 502 is fed to activation function 504. In one or more embodiments, the activation function 504 includes a non-linear activation function. In one or more embodiments, the activation function 504 includes a ReLU (Rectified Linear Unit). In one or more embodiments, the activation function 504 includes a Leaky ReLU (Rectified Linear Unit). The Leaky ReLU (Rectified Linear Unit) is a type of activation function used in artificial neural networks. It is similar to the standard ReLU function but allows a small, non-zero gradient when the input is negative, instead of setting the gradient to zero. In one or more embodiments, the Leaky ReLU activation function is defined as follows:

Where α is a small constant, such as 0.01, that determines the slope of the function for negative inputs. This can serve to reduce the probability of developing inactive neurons during training and/or operational use of the neural network.

The output of the activation function 504 can be input to another convolutional block 506. The output of convolutional block 506 can be fed to an additional activation function 508. In one or more embodiments, the activation function 508 can include a sigmoid function. The sigmoid function can be used to introduce non-linearity into the network. In one or more embodiments, the sigmoid function is defined as:

Where e is the base of the natural logarithm. The sigmoid function has a characteristic S-shaped curve that maps any real value to a value between 0 and 1. This property makes it suitable for binary classification problems, where the output can be interpreted as the probability of the input belonging to a certain class, including the identification of noise and artifacts in images, in accordance with disclosed embodiments. In one or more embodiments, the activation function 508 can include a ReLU function instead of, or in addition to, the sigmoid function. Other embodiments can include a Tanh (hyperbolic tangent) activation function, softmax activation function, swish activation function, and/or other suitable activation function.

FIG. 6 shows an example of image enhancement performed by an embodiment. Image 602 is a low-light image, containing minimal visible details. An area within the image 602, denoted by rectangle 603, is extracted as a raw input image, and processed using a system such as depicted in FIG. 2. The resulting enhanced low-light image is shown at 604. As can be seen, the enhanced low-light image reveals additional details not easily visible in the original image 602.

FIG. 7 is a method diagram illustrating an exemplary method 700 for image enhancement utilizing an adaptive denoising preprocessing module, according to an embodiment. The process begins at step 701 where a raw input image 210 is received, typically in a Bayer RGBG format from a digital camera sensor. At step 702, the image analysis subsystem 213 performs a comprehensive analysis of the input image, quantifying characteristics such as overall brightness, contrast levels, noise estimation, and detail complexity.

At step 703, the adaptation control subsystem 214 uses the analysis results to determine optimal preprocessing parameters. These parameters include the wavelet decomposition level, wavelet type selection, thresholding parameters, and subband weighting factors. At step 704, adaptive wavelet decomposition 215 is performed using the determined parameters, resulting in multiple frequency domain subimages.

At step 705, the frequency domain subimages are input to corresponding neural networks 216, 218, 220, and 222. Each network is specialized for a specific frequency range (LL, LH, HL, HH) and applies denoising and enhancement techniques appropriate for that range. At step 706, the output of the neural networks is provided to an inverse wavelet module 224, which reconstructs the enhanced image from the processed frequency domain subimages.

At step 707, the reconstructed image is provided to an image signal processing (ISP) pipeline 226 for final adjustments. These may include color correction, further contrast enhancement, and sharpening. Finally, at step 708, an enhanced low-light image 228 is produced as an output of the ISP pipeline, featuring improved brightness, reduced noise, and enhanced detail compared to the original input image.

Recursive Adaptive Wavelet Network System Architecture

FIG. 8 is a block diagram illustrating exemplary architecture of a hierarchical adaptive wavelet decomposition system 800 for low-light image enhancement, in an embodiment. System 800 receives preprocessing parameters from adaptation control subsystem 214 and processes raw image data to produce a denoised output suitable for image signal processing pipeline 226. The system comprises four primary subsystems that operate in sequence: hierarchical decomposition controller 810, cross-scale feature fusion system 820, dynamic network pool 830, and adaptive reconstruction engine 840.

In one or more embodiments, system 800 provides an alternative processing pathway to the fixed architecture described in subsystems 215-222 of the base patent. Instead of generating and processing four fixed subbands (LL, LH, HL, HH) with predetermined neural networks, system 800 introduces a hierarchical and adaptive framework that enables variable-depth decomposition, cross-scale feature integration, dynamic neural network allocation, and adaptive reconstruction. This alternative configuration allows for more precise and efficient processing of low-light images based on local image characteristics, and may be substituted for or integrated alongside the original processing flow.

Hierarchical decomposition controller 810 receives preprocessing parameters including wavelet type preferences, noise estimates, and complexity thresholds from adaptation control subsystem 214. Controller 810 performs recursive wavelet decomposition on input subimages, creating a variable-depth decomposition tree. At each node in the decomposition tree, controller 810 employs a gate network that analyzes the frequency domain content of that node. A gate network computes complexity metrics including entropy, edge density, texture coherence, local variance, and gradient magnitude. The gate network is trained using labeled low-light image datasets to learn optimal decision boundaries for decomposition depth, wavelet selection, and resource prioritization. This learning-based approach allows the system to generalize across diverse image types and adapt to unseen scenarios during inference. Based on these metrics, the gate network determines whether to further decompose the node, selects an optimal wavelet type for any subsequent decomposition, and assigns a resource allocation priority. Controller 810 maintains the tree structure with metadata for each node including decomposition depth, processing metrics, and parent-child relationships. The decomposition process continues recursively until the gate network determines that further decomposition would not be beneficial or until predefined depth limits are reached.

Cross-scale feature fusion system 820 receives the variable-depth decomposition tree from controller 810 and implements bidirectional information flow between nodes at different decomposition levels. System 820 employs multi-head attention mechanisms that compute attention weights between features at different scales, considering both frequency relationships and spatial correspondence. The multi-head attention weights and gating mechanisms are learned during training using supervised or semi-supervised learning on low-light image datasets with known high-quality references, enabling the system to discover meaningful multi-scale dependencies. The attention mechanisms operate between parent nodes and all descendant nodes, not limited to immediate children, enabling information from fine-scale details to inform coarse-scale processing and vice versa. System 820 creates learnable feature highways with gating mechanisms that control the intensity of information flow between scales. Scale-aware normalization is applied to handle magnitude differences between frequency bands, ensuring that features from different decomposition levels can be meaningfully combined. The system implements both bottom-up pathways for detail enhancement and top-down pathways for context propagation, using dilated convolutions within each scale to capture long-range dependencies. Skip connections allow information to bypass multiple decomposition levels when beneficial. The output of system 820 consists of enhanced multi-scale feature representations that incorporate information from across the decomposition tree.

Dynamic network pool 830 receives the enhanced features and decomposition tree structure from system 820 and manages the allocation of specialized neural networks for processing each active node. Pool 830 maintains a collection of pre-initialized network architectures optimized for different frequency characteristics. The selection logic analyzes the statistics of each node's content, including frequency distribution, edge orientation, and texture properties, to determine the most appropriate network architecture. Networks designed for smooth, low-frequency regions employ shallower architectures focused on gentle denoising and contrast enhancement. Networks for high-frequency detail regions utilize deeper architectures with specialized layers for preserving fine textures while removing noise. Edge-oriented networks incorporate asymmetric convolutions optimized for directional features. Pool 830 implements weight sharing between networks processing nodes with similar characteristics, reducing memory requirements while maintaining processing quality. The pool dynamically adjusts network capacity by modifying depth and width based on node complexity and available computational resources. A network cache with least-recently-used eviction policy maintains frequently accessed configurations for efficiency. For resource-constrained scenarios, pool 830 supports partial network execution where only essential layers are processed.

Adaptive reconstruction engine 840 receives the processed nodes from network pool 830 and rebuilds the enhanced image through intelligent traversal of the decomposition tree. Engine 840 implements learnable inverse wavelet transforms that adapt based on local image content rather than using fixed reconstruction filters. The learnable inverse wavelet transforms and residual connections are trained end-to-end to minimize reconstruction loss using paired noisy and clean image datasets. The regularization terms guiding reconstruction consistency are similarly optimized through training, enhancing output coherence across varying image conditions. The reconstruction process employs a priority queue that determines traversal order based on estimated impact on final image quality, ensuring that the most important features are reconstructed first. Multi-scale residual learning captures details that may have been altered during decomposition and processing, with learned residual connections that preserve information across scales. Engine 840 handles variable-depth branches through adaptive interpolation and padding strategies that maintain spatial alignment. Boundary artifacts are minimized through overlapping window processing with learned blending weights that ensure smooth transitions between regions processed at different depths. Quality-aware reconstruction allocates additional processing to visually important regions identified through perceptual metrics. For real-time applications, engine 840 supports progressive reconstruction where an initial result is quickly generated and iteratively refined as computational resources permit. The reconstruction process maintains consistency through learned regularization terms that ensure coherent output across the image. Engine 840 outputs a denoised image compatible with standard image signal processing pipeline 226, completing the hierarchical adaptive enhancement process.

In one or more embodiments, each of the subsystems 810, 820, 830, and 840 may be implemented independently or in various combinations depending on system requirements, resource constraints, or application-specific needs. For example, cross-scale feature fusion system 820 may be used in conjunction with a fixed wavelet decomposition process, or adaptive reconstruction engine 840 may be paired with a conventional neural network denoiser. The modular architecture enables flexible integration into existing image enhancement pipelines, facilitating scalable and customizable deployment across different platforms and hardware environments.

FIG. 9 is a flow diagram illustrating the hierarchical decomposition process performed by hierarchical decomposition controller 810, according to an embodiment. The process begins when the controller receives a frequency domain node from the decomposition tree 901. Gate network computation analyzes the node to extract multiple complexity metrics including entropy, edge density, texture coherence, local variance, and gradient magnitude 902. These metrics are combined into a complexity score that is compared against an adaptive threshold derived from preprocessing parameters 903. A decomposition decision determines whether the complexity score exceeds the threshold 904. If the score falls below the threshold, the node is marked as a leaf node and processing terminates for that branch 905. When the score exceeds the threshold, the system selects an optimal wavelet type based on the node's frequency characteristics, choosing between Haar, Daubechies, or Coiflet wavelets 906. The selected wavelet decomposition is applied to create four child nodes 907. The child nodes representing LL, LH, HL, and HH frequency subbands are added to the decomposition tree structure with appropriate metadata 908. Each child node is queued for recursive processing 909, with the process returning to the initial receiving step for each queued node until all branches reach termination conditions.

FIG. 10 is a flow diagram illustrating the cross-scale attention mechanism implemented by cross-scale feature fusion system 820, according to an embodiment. The process begins when the system receives the decomposition tree with all active nodes from hierarchical decomposition controller 810 at step 1001. Parent and descendant node pairs—spanning immediate and non-immediate relationships—are identified for cross-scale attention processing 1002. Spatial correspondence maps are computed to align features across scales 1003. Feature vectors are extracted from each node in the pair 1004. Multi-head attention computation calculates attention weights between parent and descendant features, with each head focusing on distinct inter-scale relationships 1005. Learned scale normalization is applied to reconcile magnitude differences across frequency bands 1006. Bidirectional attention pathways enable features to flow from parent to descendant nodes and vice versa 1007. Aggregated attention outputs are formed by combining results from all attention heads 1008. Gating mechanisms, trained to regulate cross-scale information flow, control the intensity of these interactions 1009. Skip connections propagate information directly across multiple decomposition levels where beneficial 1010. The fused multi-scale features are output to dynamic network pool 830 for node-level processing 1011. This process repeats for all eligible node pairs in the decomposition tree to achieve global cross-scale feature integration 1012.

FIG. 11 is a flow diagram illustrating the dynamic network allocation process performed by dynamic network pool 830, according to an embodiment. The process begins when the pool receives enhanced multi-scale features and the decomposition tree from cross-scale feature fusion system 820 at step 1101. Node characteristics are analyzed to determine frequency content type, including smooth regions, edge features, or texture patterns 1102. Each node in the decomposition tree is independently assigned a specialized neural network based on its frequency profile, enabling fine-grained processing tailored to local image content. The system queries the network cache to check if a suitable pre-configured network exists for the identified characteristics 1103. A cache hit determination evaluates whether an appropriate network was found 1104. If a cache hit occurs, the cached network is retrieved and its weights are loaded for immediate use 1105. If no suitable cached network exists, the pool selects an optimal network architecture from available templates based on the node's frequency characteristics 1106. Templates and weight sharing rules may be learned from prior training. Network capacity adjustment modifies the selected architecture's depth and width parameters based on node complexity and available computational resources 1107. Weight sharing analysis determines if the new network can share parameters with existing active networks processing similar content 1108. The network is initialized with either shared weights from similar networks or newly initialized parameters 1109. The configured network processes its assigned node to produce enhanced frequency domain output 1110. In one or more embodiments, network assignments may be scheduled for concurrent execution across nodes, leveraging hardware-level parallelism to improve processing efficiency and throughput. Processed results are collected from all allocated networks across the decomposition tree 1111. The network configuration is added to the cache for potential reuse with least-recently-used eviction if needed 1112. All processed nodes are output to adaptive reconstruction engine 840 for final image assembly 1113.

FIG. 12 is a flow diagram illustrating the adaptive reconstruction process performed by adaptive reconstruction engine 840, according to an embodiment. In one or more embodiments, reconstruction may be executed in a streaming or tiled manner, allowing for early output rendering while remaining nodes are processed. The process begins when the engine receives all processed nodes from dynamic network pool 830 at step 1201. A priority queue is initialized based on each node's estimated impact on final image quality, considering factors such as decomposition depth and content importance 1202. The highest priority node is selected from the queue for reconstruction processing 1203. Learnable inverse wavelet transform coefficients are retrieved based on the node's content characteristics and decomposition level 1204. The inverse transform coefficients and residual learning filters are trained on low-light datasets to optimize perceptual reconstruction quality and coherence across scales. The inverse transform is applied to the node using the adaptive coefficients rather than fixed reconstruction filters 1205. Multi-scale residual learning captures and preserves details that may have been altered during the decomposition and processing stages 1206. Spatial alignment verification ensures proper registration between the reconstructed region and previously processed areas 1207. Overlapping window processing with learned blending weights minimizes boundary artifacts between adjacent reconstructed regions 1208. Local quality metrics are computed to assess reconstruction fidelity in the processed region 1209. A quality threshold check determines if the reconstruction meets predetermined quality criteria 1210. If quality is insufficient, refinement parameters are adjusted and the node is requeued for additional processing 1211. If quality criteria are met, the reconstructed region is integrated into the output image buffer 1212. The process checks whether all nodes have been reconstructed 1213. If nodes remain, the process returns to select the next highest priority node 1214. Once all nodes are processed, the complete denoised image is output to image signal processing pipeline 226 at step 1215.

FIG. 13 is a flow diagram illustrating the gate network decision process within hierarchical decomposition controller 810, according to an embodiment. The process begins when the gate network receives a frequency domain node for evaluation 1301. Entropy calculation quantifies the information content and randomness within the node's frequency data 1302. Edge detection algorithms identify and count edge pixels to determine edge density throughout the node 1303. Texture analysis employs autocorrelation functions to measure pattern regularity and coherence 1304. Statistical moments including mean, variance, skewness, and kurtosis are computed across the node's spatial extent 1305. Statistical analysis computes key moments (e.g., variance, skewness) to describe intensity distribution. Gradient analysis calculates directional derivatives to identify regions of rapid intensity change 1306. Each metric is normalized to a common scale using learned normalization parameters 1307. A weighted combination of metrics produces a unified complexity score using learned weighting factors 1308. The weighting factors are learned during training on labeled low-light image datasets to optimize accuracy of the complexity assessment. The preprocessing parameters are consulted to retrieve the adaptive threshold for the current decomposition level 1309. Threshold adjustment factors are applied based on global image characteristics and decomposition depth 1310. The gate network compares the complexity score against the adjusted threshold 1311. If the score exceeds the threshold, the network selects an optimal wavelet type by evaluating metric patterns against learned wavelet suitability criteria 1312. Resource allocation priority is assigned based on the complexity score magnitude and current system load 1313. The gate network outputs its three-part decision consisting of decomposition recommendation, wavelet type selection, and resource priority 1314. This three-part decision enables adaptive, localized control over decomposition, supporting efficient and content-aware wavelet processing throughout the hierarchy.

FIG. 14 is a flow diagram illustrating the complete system data flow through hierarchical adaptive wavelet decomposition system 800, according to an embodiment. The process begins when raw image data is received from image analysis subsystem 213 along with preprocessing parameters from adaptation control subsystem 214 at step 1401. The raw image is divided into subsampled subimages in Bayer RGBG format 1402. Hierarchical decomposition controller 810 initiates recursive wavelet decomposition on each subimage 1403. Gate networks at each decomposition node evaluate complexity metrics and make decomposition decisions, creating a variable-depth tree structure 1404. The completed decomposition tree with all frequency domain nodes is passed to cross-scale feature fusion system 820 at step 1405. System 820 implements attention mechanisms between nodes at different decomposition levels, creating bidirectional feature pathways 1406. Enhanced multi-scale features incorporating cross-level information are generated through attention aggregation and feature highway gating 1407. Dynamic network pool 830 receives the enhanced features and allocates specialized neural networks to each node based on frequency characteristics 1408. Neural network assignments may be executed in parallel across nodes for improved throughput. Networks process their assigned nodes with potential weight sharing between similar nodes for efficiency 1409. Processed frequency domain outputs from all allocated networks are collected 1410. Adaptive reconstruction engine 840 receives all processed nodes and initializes priority-based reconstruction 1411. The engine traverses the decomposition tree applying learnable inverse transforms and multi-scale residual learning 1412. Quality feedback mechanisms ensure reconstruction fidelity with potential refinement iterations 1413. Local quality metrics guide refinement, with subtrees selectively reprocessed when thresholds are unmet. The complete denoised image is assembled from all reconstructed regions 1414. The enhanced image is output to image signal processing pipeline 226 in standard format 1415. This hierarchical adaptive processing framework enables superior low-light image enhancement with efficient, content-aware resource allocation and seamless ISP pipeline integration.

The figures and accompanying descriptions are intended to illustrate exemplary embodiments and should not be interpreted as limiting the scope of the invention. The components, steps, and interconnections shown in the diagrams may be rearranged, combined, or substituted with functionally equivalent elements without departing from the scope of the disclosed subject matter. While certain architectural sequences, metric calculations, or network configurations are described in detail, those skilled in the art will recognize that variations may be made in architecture, parameterization, or implementation platform without affecting the essential operation of the system. The embodiments described herein encompass both hardware and software implementations and are compatible with multiple deployment environments including mobile, embedded, and cloud-based systems.

While the embodiments disclosed herein are described primarily in the context of low-light image enhancement, the underlying hierarchical adaptive wavelet processing architecture is broadly applicable to a range of image and signal processing tasks. These may include image denoising, super-resolution, dehazing, HDR tone mapping, medical image enhancement, satellite imagery analysis, infrared image reconstruction, and video frame interpolation. The recursive decomposition strategy, cross-scale feature fusion, and adaptive reconstruction mechanisms described herein may be applied to grayscale, multispectral, or hyperspectral data formats, and may be used in both still image and video pipelines. Accordingly, the scope of the invention should not be limited to low-light enhancement alone, but should be understood to encompass any application where adaptive, content-aware multi-resolution analysis and reconstruction provide advantages in image quality, efficiency, or feature preservation.

Exemplary Computing Environment

FIG. 15 illustrates an exemplary computing environment on which an embodiment described herein may be implemented, in full or in part. This exemplary computing environment describes computer-related components and processes supporting enabling disclosure of computer-implemented embodiments. Inclusion in this exemplary computing environment of well-known processes and computer components, if any, is not a suggestion or admission that any embodiment is no more than an aggregation of such processes or components. Rather, implementation of an embodiment using processes and components described in this exemplary computing environment will involve programming or configuration of such processes and components resulting in a machine specially programmed or configured for such implementation. The exemplary computing environment described herein is only one example of such an environment and other configurations of the components and processes are possible, including other relationships between and among components, and/or absence of some processes or components described. Further, the exemplary computing environment described herein is not intended to suggest any limitation as to the scope of use or functionality of any embodiment implemented, in whole or in part, on components or processes described herein.

The exemplary computing environment described herein comprises a computing device 10 (further comprising a system bus 11, one or more processors 20, a system memory 30, one or more interfaces 40, one or more non-volatile data storage devices 50), external peripherals and accessories 60, external communication devices 70, remote computing devices 80, and cloud- based services 90.

System bus 11 couples the various system components, coordinating operation of and data transmission between those various system components. System bus 11 represents one or more of any type or combination of types of wired or wireless bus structures including, but not limited to, memory busses or memory controllers, point-to-point connections, switching fabrics, peripheral busses, accelerated graphics ports, and local busses using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) busses, Micro Channel Architecture (MCA) busses, Enhanced ISA (EISA) busses, Video Electronics Standards Association (VESA) local busses, a Peripheral Component Interconnects (PCI) busses also known as a Mezzanine busses, or any selection of, or combination of, such busses. Depending on the specific physical implementation, one or more of the processors 20, system memory 30 and other components of the computing device 10 can be physically co-located or integrated into a single physical component, such as on a single chip. In such a case, some or all of system bus 11 can be electrical pathways within a single chip structure.

Computing device may further comprise externally-accessible data input and storage devices 12 such as compact disc read-only memory (CD-ROM) drives, digital versatile discs (DVD), or other optical disc storage for reading and/or writing optical discs 62; magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices; or any other medium which can be used to store the desired content and which can be accessed by the computing device 10. Computing device may further comprise externally-accessible data ports or connections 12 such as serial ports, parallel ports, universal serial bus (USB) ports, and infrared ports and/or transmitter/receivers. Computing device may further comprise hardware for wireless communication with external devices such as IEEE 1394 (“Firewire”) interfaces, IEEE 802.11 wireless interfaces, BLUETOOTH® wireless interfaces, and so forth. Such ports and interfaces may be used to connect any number of external peripherals and accessories 60 such as visual displays, monitors, and touch-sensitive screens 61, USB solid state memory data storage drives (commonly known as “flash drives” or “thumb drives”) 63, printers 64, pointers and manipulators such as mice 65, keyboards 66, and other devices 67 such as joysticks and gaming pads, touchpads, additional displays and monitors, and external hard drives (whether solid state or disc-based), microphones, speakers, cameras, and optical scanners.

Processors 20 are logic circuitry capable of receiving programming instructions and processing (or executing) those instructions to perform computer operations such as retrieving data, storing data, and performing mathematical calculations. Processors 20 are not limited by the materials from which they are formed or the processing mechanisms employed therein, but are typically comprised of semiconductor materials into which many transistors are formed together into logic gates on a chip (i.e., an integrated circuit or IC). The term processor includes any device capable of receiving and processing instructions including, but not limited to, processors operating on the basis of quantum computing, optical computing, mechanical computing (e.g., using nanotechnology entities to transfer data), and so forth. Depending on configuration, computing device 10 may comprise more than one processor. For example, computing device 10 may comprise one or more central processing units (CPUs) 21, each of which itself has multiple processors or multiple processing cores, each capable of independently or semi-independently processing programming instructions. Further, computing device 10 may comprise one or more specialized processors such as a graphics processing unit (GPU) 22 configured to accelerate processing of computer graphics and images via a large array of specialized processing cores arranged in parallel.

System memory 30 is processor-accessible data storage in the form of volatile and/or nonvolatile memory. System memory 30 may be either or both of two types: non-volatile memory and volatile memory. Non-volatile memory 30a is not erased when power to the memory is removed, and includes memory types such as read only memory (ROM), electronically-erasable programmable memory (EEPROM), and rewritable solid-state memory (commonly known as “flash memory”). Non-volatile memory 30a is typically used for long-term storage of a basic input/output system (BIOS) 31, containing the basic instructions, typically loaded during computer startup, for transfer of information between components within computing device, or a unified extensible firmware interface (UEFI), which is a modern replacement for BIOS that supports larger hard drives, faster boot times, more security features, and provides native support for graphics and mouse cursors. Non-volatile memory 30a may also be used to store firmware comprising a complete operating system 35 and applications 36 for operating computer-controlled devices. The firmware approach is often used for purpose-specific computer-controlled devices such as appliances and Internet-of-Things (IoT) devices where processing power and data storage space is limited. Volatile memory 30b is erased when power to the memory is removed and is typically used for short-term storage of data for processing. Volatile memory 30b includes memory types such as random-access memory (RAM), and is normally the primary operating memory into which the operating system 35, applications 36, program modules 37, and application data 38 are loaded for execution by processors 20. Volatile memory 30b is generally faster than non-volatile memory 30a due to its electrical characteristics and is directly accessible to processors 20 for processing of instructions and data storage and retrieval. Volatile memory 30b may comprise one or more smaller cache memories which operate at a higher clock speed and are typically placed on the same IC as the processors to improve performance.

Interfaces 40 may include, but are not limited to, storage media interfaces 41, network interfaces 42, display interfaces 43, and input/output interfaces 44. Storage media interface 41 provides the necessary hardware interface for loading data from non-volatile data storage devices 50 into system memory 30 and storage data from system memory 30 to non-volatile data storage device 50. Network interface 42 provides the necessary hardware interface for computing device 10 to communicate with remote computing devices 80 and cloud-based services 90 via one or more external communication devices 70. Display interface 43 allows for connection of displays 61, monitors, touchscreens, and other visual input/output devices. Display interface 43 may include a graphics card for processing graphics-intensive calculations and for handling demanding display requirements. Typically, a graphics card includes a graphics processing unit (GPU) and video RAM (VRAM) to accelerate display of graphics. One or more input/output

(I/O) interfaces 44 provide the necessary support for communications between computing device 10 and any external peripherals and accessories 60. For wireless communications, the necessary radio-frequency hardware and firmware may be connected to I/O interface 44 or may be integrated into I/O interface 44.

Non-volatile data storage devices 50 are typically used for long-term storage of data. Data on non-volatile data storage devices 50 is not erased when power to the non-volatile data storage devices 50 is removed. Non-volatile data storage devices 50 may be implemented using any technology for non-volatile storage of content including, but not limited to, CD-ROM drives, digital versatile discs (DVD), or other optical disc storage; magnetic cassettes, magnetic tape, magnetic disc storage, or other magnetic storage devices; solid state memory technologies such as EEPROM or flash memory; or other memory technology or any other medium which can be used to store data without requiring power to retain the data after it is written. Non-volatile data storage devices 50 may be non-removable from computing device 10 as in the case of internal hard drives, removable from computing device 10 as in the case of external USB hard drives, or a combination thereof, but computing device will typically comprise one or more internal, non-removable hard drives using either magnetic disc or solid-state memory technology. Non-volatile data storage devices 50 may store any type of data including, but not limited to, an operating system 51 for providing low-level and mid-level functionality of computing device 10, applications 52 for providing high-level functionality of computing device 10, program modules 53 such as containerized programs or applications, or other modular content or modular programming, application data 54, and databases 55 such as relational databases, non-relational databases, object oriented databases, BOSQL databases, and graph databases.

Applications (also known as computer software or software applications) are sets of programming instructions designed to perform specific tasks or provide specific functionality on a computer or other computing devices. Applications are typically written in high-level programming languages such as C++, Java, and Python, which are then either interpreted at runtime or compiled into low-level, binary, processor-executable instructions operable on processors 20. Applications may be containerized so that they can be run on any computer hardware running any known operating system. Containerization of computer software is a method of packaging and deploying applications along with their operating system dependencies into self-contained, isolated units known as containers. Containers provide a lightweight and consistent runtime environment that allows applications to run reliably across different computing environments, such as development, testing, and production systems.

The memories and non-volatile data storage devices described herein do not include communication media. Communication media are means of transmission of information such as modulated electromagnetic waves or modulated data signals configured to transmit, not store, information. By way of example, and not limitation, communication media includes wired communications such as sound signals transmitted to a speaker via a speaker wire, and wireless communications such as acoustic waves, radio frequency (RF) transmissions, infrared emissions, and other wireless media.

External communication devices 70 are devices that facilitate communications between computing device and either remote computing devices 80, or cloud-based services 90, or both. External communication devices 70 include, but are not limited to, data modems 71 which facilitate data transmission between computing device and the Internet 75 via a common carrier such as a telephone company or internet service provider (ISP), routers 72 which facilitate data transmission between computing device and other devices, and switches 73 which provide direct data communications between devices on a network. Here, modem 71 is shown connecting computing device 10 to both remote computing devices 80 and cloud-based services 90 via the Internet 75. While modem 71, router 72, and switch 73 are shown here as being connected to network interface 42, many different network configurations using external communication devices 70 are possible. Using external communication devices 70, networks may be configured as local area networks (LANs) for a single location, building, or campus, wide area networks (WANs) comprising data networks that extend over a larger geographical area, and virtual private networks (VPNs) which can be of any size but connect computers via encrypted communications over public networks such as the Internet 75. As just one exemplary network configuration, network interface 42 may be connected to switch 73 which is connected to router 72 which is connected to modem 71 which provides access for computing device 10 to the Internet 75. Further, any combination of wired 77 or wireless 76 communications between and among computing device 10, external communication devices 70, remote computing devices 80, and cloud-based services 90 may be used. Remote computing devices 80, for example, may communicate with computing device through a variety of communication channels 74 such as through switch 73 via a wired 77 connection, through router 72 via a wireless connection 76, or through modem 71 via the Internet 75. Furthermore, while not shown here, other hardware that is specifically designed for servers may be employed. For example, secure socket layer (SSL) acceleration cards can be used to offload SSL encryption computations, and transmission control protocol/internet protocol (TCP/IP) offload hardware and/or packet classifiers on network interfaces 42 may be installed and used at server devices.

In a networked environment, certain components of computing device 10 may be fully or partially implemented on remote computing devices 80 or cloud-based services 90. Data stored in non-volatile data storage device 50 may be received from, shared with, duplicated on, or offloaded to a non-volatile data storage device on one or more remote computing devices 80 or in a cloud computing service 92. Processing by processors 20 may be received from, shared with, duplicated on, or offloaded to processors of one or more remote computing devices 80 or in a distributed computing service 93. By way of example, data may reside on a cloud computing service 92, but may be usable or otherwise accessible for use by computing device 10. Also, certain processing subtasks may be sent to a microservice 91 for processing with the result being transmitted to computing device 10 for incorporation into a larger processing task. Also, while components and processes of the exemplary computing environment are illustrated herein as discrete units (e.g., OS 51 being stored on non-volatile data storage device 51 and loaded into system memory 35 for use) such processes and components may reside or be processed at various times in different components of computing device 10, remote computing devices 80, and/or cloud-based services 90.

In an implementation, the disclosed systems and methods may utilize, at least in part, containerization techniques to execute one or more processes and/or steps disclosed herein. Containerization is a lightweight and efficient virtualization technique that allows you to package and run applications and their dependencies in isolated environments called containers. One of the most popular containerization platforms is Docker, which is widely used in software development and deployment. Containerization, particularly with open-source technologies like Docker and container orchestration systems like Kubernetes, is a common approach for deploying and managing applications. Containers are created from images, which are lightweight, standalone, and executable packages that include application code, libraries, dependencies, and runtime. Images are often built from a Dockerfile or similar, which contains instructions for assembling the image. Dockerfiles are configuration files that specify how to build a Docker image. Systems like Kubernetes also support containers or CRI-O. They include commands for installing dependencies, copying files, setting environment variables, and defining runtime configurations. Docker images are stored in repositories, which can be public or private. Docker Hub is an exemplary public registry, and organizations often set up private registries for security and version control using tools such as Hub, JFrog Artifactory and Bintray, Github

Packages or Container registries. Containers can communicate with each other and the external world through networking. Docker provides a bridge network by default, but can be used with custom networks. Containers within the same network can communicate using container names or IP addresses.

Remote computing devices 80 are any computing devices not part of computing device 10. Remote computing devices 80 include, but are not limited to, personal computers, server computers, thin clients, thick clients, personal digital assistants (PDAs), mobile telephones, watches, tablet computers, laptop computers, multiprocessor systems, microprocessor based systems, set-top boxes, programmable consumer electronics, video game machines, game consoles, portable or handheld gaming units, network terminals, desktop personal computers

(PCs), minicomputers, main frame computers, network nodes, virtual reality or augmented reality devices and wearables, and distributed or multi-processing computing environments. While remote computing devices 80 are shown for clarity as being separate from cloud-based services 90, cloud-based services 90 are implemented on collections of networked remote computing devices 80.

Cloud-based services 90 are Internet-accessible services implemented on collections of networked remote computing devices 80. Cloud-based services are typically accessed via application programming interfaces (APIs) which are software interfaces which provide access to computing services within the cloud-based service via API calls, which are pre-defined protocols for requesting a computing service and receiving the results of that computing service. While cloud-based services may comprise any type of computer processing or storage, three common categories of cloud-based services 90 are microservices 91, cloud computing services 92, and distributed computing services 93.

Microservices 91 are collections of small, loosely coupled, and independently deployable computing services. Each microservice represents a specific computing functionality and runs as a separate process or container. Microservices promote the decomposition of complex applications into smaller, manageable services that can be developed, deployed, and scaled independently. These services communicate with each other through well-defined application programming interfaces (APIs), typically using lightweight protocols like HTTP, gRPC, or message queues such as Kafka. Microservices 91 can be combined to perform more complex processing tasks.

Cloud computing services 92 are delivery of computing resources and services over the Internet 75 from a remote location. Cloud computing services 92 provide additional computer hardware and storage on as-needed or subscription basis. Cloud computing services 92 can provide large amounts of scalable data storage, access to sophisticated software and powerful server-based processing, or entire computing infrastructures and platforms. For example, cloud computing services can provide virtualized computing resources such as virtual machines, storage, and networks, platforms for developing, running, and managing applications without the complexity of infrastructure management, and complete software applications over the Internet on a subscription basis.

Distributed computing services 93 provide large-scale processing using multiple interconnected computers or nodes to solve computational problems or perform tasks collectively. In distributed computing, the processing and storage capabilities of multiple machines are leveraged to work together as a unified system. Distributed computing services are designed to address problems that cannot be efficiently solved by a single computer or that require large-scale computational power. These services enable parallel processing, fault tolerance, and scalability by distributing tasks across multiple nodes.

Although described above as a physical device, computing device 10 can be a virtual computing device, in which case the functionality of the physical components herein described, such as processors 20, system memory 30, network interfaces 40, and other like components can be provided by computer-executable instructions. Such computer-executable instructions can execute on a single physical computing device, or can be distributed across multiple physical computing devices, including being distributed across multiple physical computing devices in a dynamic manner such that the specific, physical computing devices hosting such computer-executable instructions can dynamically change over time depending upon need and availability. In the situation where computing device 10 is a virtualized device, the underlying physical computing devices hosting such a virtualized computing device can, themselves, comprise physical components analogous to those described above, and operating in a like manner. Furthermore, virtual computing devices can be utilized in multiple layers with one virtual computing device executing within the construct of another virtual computing device. Thus, computing device 10 may be either a physical computing device or a virtualized computing device within which computer-executable instructions can be executed in a manner consistent with their execution by a physical computing device. Similarly, terms referring to physical components of the computing device, as utilized herein, mean either those physical components or virtualizations thereof performing the same or equivalent functions.

As can now be appreciated, disclosed embodiments enhance low-light images, allowing details that are difficult to see in the original image to become more visible. Enhancement of low-light images can be particularly useful for surveillance, security, and forensic applications, where identifying objects, people, or text in low-light conditions is important. In images containing text, such as documents or signs, enhancing the image can make the text more readable. This can be helpful in scenarios where capturing clear text is crucial, such as reading license plates or identifying street signs in low-light conditions. In photography and video recording, enhancing low-light images can improve the overall aesthetics of the image or video by making details and colors more vibrant and clearer. With the feature of enhancing low-light images, disclosed embodiments can have a wide range of applications across various industries, improving visibility, readability, and image quality in challenging lighting conditions.